- Open Access
A human-computer collaborative workflow for the acquisition and analysis of terrestrial insect movement in behavioral field studies
© Reda et al.; licensee Springer. 2013
- Received: 30 January 2013
- Accepted: 1 August 2013
- Published: 13 August 2013
The study of insect behavior from video sequences poses many challenges. Despite the advances in image processing techniques, the current generation of insect tracking tools is only effective in controlled lab environments and under ideal lighting conditions. Very few tools are capable of tracking insects in outdoor environments where the insects normally operate. Furthermore, the majority of tools focus on the first stage of the analysis workflow, namely the acquisition of movement trajectories from video sequences. Far less effort has gone into developing specialized techniques to characterize insect movement patterns once acquired from videos. In this paper, we present a human-computer collaborative workflow for the acquisition and analysis of insect behavior from field-recorded videos. We employ a human-guided video processing method to identify and track insects from noisy videos with dynamic lighting conditions and unpredictable visual scenes, improving tracking precision by 20% to 44% compared to traditional automated methods. The workflow also incorporates a novel visualization tool for the large-scale exploratory analysis of insect trajectories. We also provide a number of quantitative methods for statistical hypothesis testing. Together, the various components of the workflow provide end-to-end quantitative and qualitative methods for the study of insect behavior from field-recorded videos. We demonstrate the effectiveness of the proposed workflow with a field study on the navigational strategies of Kenyan seed harvester ants.
- Insect tracking
- Ecological field studies
- Human-computer symbiosis
- Trajectory analysis
Characterizing and understanding insect movement patterns is a challenging endeavor. Due to the stochastic nature of insect motion, researchers often need to analyze large trajectory datasets that capture their movement under diverse conditions to accurately interpret their behavior. Automated image processing techniques have therefore become very popular among entomologists and behavioral ecologists as a way of quickly acquiring large datasets of insect trajectories from video. Nevertheless, extracting and quantifying the behavior of the focal insects with sufficient accuracy remains difficult due to the limitations of current image processing techniques. Consequently, in the vast majority of studies, researchers perform their experiments in controlled indoor labs and under ideal lighting conditions to reduce noise and improve tracking accuracy. Lab-based experiments, however, may radically alter the landscape and stimuli that insects normally encounter in their native habitat, casting doubts on the ecological validity of such experiments. Furthermore, many environmental variables are extremely difficult to replicate in the lab. For example, studies involving insect navigation often have to be carried out in the field, as the natural landscape plays a crucial role in providing navigational cues to insects. Yet, very few techniques have been proposed to acquire and quantify insect motion patterns in natural settings, with the exception of tracking honeybees in hives. To our knowledge, no robust techniques have been proposed to acquire the movement of terrestrial insects in the field.
A novel, human-guided image processing pipeline to extract and track insects in outdoor field environments with high levels of noise
A novel trajectory visualization tool for the exploratory and qualitative analysis of insect behavioral patterns
Quantitative analysis methods for statistical testing of hypotheses and spatiotemporal movement regularities in insect motion trajectories
The flexibility of the proposed workflow makes it uniquely suited for field entomologists and experimental ecologists; unlike existing tools, our image processing pipeline does not presume long uninterrupted observational periods, making it suitable for behavioral assays that require repeated active manipulation of the insects and their surroundings in their natural habitat. In the rest of this paper, we discuss the limitations of existing techniques and show how our workflow addresses them in Section 2. We present the individual components of the workflow and describe how they are integrated in Section 3. In Section 4 we illustrate the effectiveness of the proposed workflow with a real-world use case involving a field study of the navigational strategies of Kenyan seed harvester and demonstrate the precision of the proposed human-guided video analysis technique. We discuss the current limitations of the workflow and planned future research in Section 5 and conclude the paper in Section 6.
The study of insect behavior relies largely on behavioral assays. The movement of individual insects can be extremely informative as to the nature of navigational strategies and decision making processes (reviewed in [1–3]). However, many previous studies have been somewhat limited in their scope due to the lack of workflows for collecting, processing, and analyzing trajectories in the field. The observational methods that ecologists and biologists use to collect data have constraints on the resolution of trajectory information that can be collected in field experiments; even recent studies primarily rely only on the measured orientations of moving insects rather than exploiting full trajectories (e.g., [3–5]). It has long been recognized that the distribution of orientations and turning angles making up an insect’s trajectory promises to contain much more information about the behavioral rules governing navigation (e.g., ). Such detailed information has traditionally been collected by hand (e.g., ) or by moving cameras in order to keep the focal insect at the center of the viewfinder and then inferring position from the tilt and azimuth angles (reviewed in ) - both relatively time-consuming methods. The lack of computational techniques that allow accurate acquisition and analysis of insect trajectories in the field has largely impeded the research on many interesting problems in behavioral entomology.
There is a wealth of image analysis methods for tracking insects in videos recorded under highly controlled conditions. For instance, Balch et al. described an algorithm to track ants in special containers with ideal lighting conditions . SwisTrack is another widely used tool for tracking insects and small robots . Its modular architecture allows for configurable image processing pipelines that can be built from basic components (e.g., background subtraction, blob detection, particle tracking). Beetrack is a similar software tool with a more advanced toolset for the analysis of honeybees’ locomotion . While the aforementioned tools provide fully automated insect tracking, they require controlled environments along with a predetermined set of parameters, making them unsuitable for outdoor field studies where the lighting conditions are constantly changing and the visual field is susceptible to frequent intrusion from other insects. To the best of our knowledge, no one has successfully used any of them to track insects in their native habitat.
Statistically inspired approaches have been developed in an attempt to overcome the limitations of traditional image processing pipelines. Khan et al. developed an effective particle tracking system using Markov chain Monte Carlo . Their method is capable of tracking interacting agents demonstrating good results when used to track ants in the lab. Kimura et al. described a novel technique based on vector quantization to track large numbers of densely packed honeybees in hives .
Despite their attractiveness, automated image processing methods are susceptible to many sources of error which have the potential to significantly degrade the accuracy of the extracted trajectories. Collaborative human-computer approaches have been proposed to improve accuracy in situations that are difficult to resolve by the computer alone . For example, DeCamp and Roy relied on a human operator to annotate preprocessed video segments to track human activities in indoor spaces . Li et al. employed a fully automated image processing algorithm to track migrating cells and later relied on a human operator to correct errors in trajectories . Voss and Zeil described a technique to extract the three-dimensional (3D) motion of flying insects under natural light conditions, requesting human intervention in ambiguous situations . We also employ a human-computer collaboration paradigm relying on a human operator to tag the initial location of the focal insect and letting the computer perform automatic image processing and tracking where possible and asking for human input when ambiguities occur.
Once insects are recognized and their motion tracked and recorded in the form of individual trajectories, the next task is to analyze those trajectories to discover and characterize consistent behavioral patterns the insects exhibit. Many automated techniques have been proposed to quantitatively analyze the motion of insects and animals. For instance, a data-driven Markov chain Monte Carlo has been employed to infer temporal patterns in the motion of bees . The k-means clustering of movement-based feature vectors has been used to recognize distinct behavioral patterns exhibited by grasshoppers . Time series analysis was used to recognize distinct behavioral states in leeches . However, to the best of our knowledge, no techniques have been proposed for exploratory, human-guided qualitative analysis of insect motion aside from simple observations with the naked eye. This is important as exploratory analysis has the potential to reveal behavioral patterns that may be difficult to recognize and interpret from statistical data alone . We address this gap in the literature by providing a novel interactive visualization tool to explore and visually analyze large collections of insect trajectories. Once qualitative patterns are detected, they can be quantitatively tested for statistical significance in the final stage of the workflow.
In summary, previous works on acquiring and analyzing insect movement have mostly focused on automatic, passive observations of insect collectives in highly controlled environments and under ideal lighting conditions. Yet, in many cases, the focal behavior is largely dependent on the natural habitat of the insect and thus can only be studied in the field. Moreover, field entomologists often need to actively and repeatedly manipulate the insects and their surrounding environment in order to elicit responses for specific stimuli or situations. This renders the majority of existing tools unsuitable as they often assume long, uninterrupted observational periods. The workflow we propose in this paper addresses these issues and targets studies where researchers need to record, extract, and analyze large collections of insect trajectories under a variety of experimental conditions. Furthermore, we also address the problem of actually analyzing and making sense of those trajectories once they are recorded. By integrating interactive visual exploration and statistical analysis of trajectory features, our workflow supports both qualitative and quantitative analyses.
Field research poses unique challenges that are not normally encountered in the lab. The stringent time and budgetary constraints combined with the remoteness of many field sites place additional emphasis on the quality and value of every experiment. Such experiments typically have to be performed manually - often with one insect at a time - to isolate the relevant variables and to accurately characterize the behavior at the individual level. While efficient data acquisition is desirable, field researchers often place a higher value on the reliability and accuracy of the data, due to the considerably high cost of field studies and the difficulty in replicating them. Yet, compared to lab-based research, field experiments are unpredictable in nature and suffer significantly lower signal-to-noise ratios, making accurate video analysis even more challenging. For example, the lighting conditions are far more dynamic in the field and the visual scene is susceptible to interference and intrusion from grasses, shadows, and even other insects or animals.
The manual, high-cost, narrow-band nature of field experiments combined with the increased level of noise call for workflows that prioritize data accuracy and resolution over throughput. Human-computer collaborative systems provide a good compromise to address these challenges . In this paradigm, a human analyst and a computer work collaboratively to complete the task; the computer performs laborious tasks, such as detecting and tracking insects in image sequences, while the human provides guidance and intervention in difficult and ambiguous situations, such as noisy images that are difficult for the computer to resolve. Furthermore, a human-computer collaborative workflow can potentially facilitate high-level qualitative analysis of the data by leveraging human judgment and interpretation. One could envision an interactive system where a researcher contemplates theories regarding a hypothesized or observed behavior with the computer providing the means to quickly query large collections of trajectories, enabling the researcher to weigh the data against his/her hypotheses in a visual and qualitative manner. When a number of promising hypotheses have been formulated, the computer can test those hypotheses quantitatively by performing computational and statistical tests on various trajectory features.
Although human-computer collaborative workflows do require increased involvement of researchers throughout the analysis, we believe that this active involvement translates to more accurate data acquisition as well as improved understanding of the underlying insect behavior. The key to building effective workflows is designing interactive visual interfaces that allow researchers to ‘see’ the data, supply judgment and interpretation, and intervene to correct errors and artifacts produced by the computer.
3.2 Human-guided video processing and insect tracking
Lighting conditions, shadows, moving debris, soil color, and other encroaching insects and animals can adversely affect the quality of data in field studies. For instance, moving grasses may cast shadows, causing false positives and interfering with tracking. To achieve accurate tracking, we employ an automated algorithm that performs the bulk of the image processing and insect tracking coupled with an interactive user interface that enables the human operator to watch the algorithm's output and intervene to rectify errors in the tracking. The program takes control from the automated algorithm and hands it to the operator to take action when needed. Control is handed back to the algorithm when the ambiguity is resolved. The operator may also initiate corrective intervention when errors in the tracking are observed. We first describe the video processing pipeline, discuss the tracking algorithm, and then describe how a human operator can intervene to rectify problems in tracking.
3.2.1 Video processing
The pipeline takes a video feed as an input, subtracts the background, filters each frame for noise and artifacts, and outputs the moving parts as binary blobs of pixels. Figure 2 illustrates this process. The following is a description of the individual steps:
Background elimination. We employ the foreground object detection algorithm described by Li et al. to segment moving insects from the background scene , giving us a binary image indicating the location of insects in the frame. Ideally, this step would completely eliminate the background leaving only moving insects. In most cases, however, the visual scene is simply too noisy, resulting in false-positive blobs from debris, grasses, and shadows.
Masking. The non-interesting parts of the image are removed to ease the task of the tracking algorithm and remove unwanted noise and artifacts. For example, if the experimenter is using a marked experimental arena to conduct the experiments, the surroundings can be removed. The mask has to be manually updated whenever the camera or subject position changes.
Noise filtering. The frame is processed to remove unwanted noise and artifacts. We first apply a Median Blur filter to remove ‘salt and pepper noise’ . To further smooth, the video feed is blurred using a 3 × 3 Gaussian kernel, after which the frame is thresholded to obtain a binary image.
Insect shadow elimination. Insects may cast prominent shadows on the ground, which tend to confuse the tracking algorithm and cause it to jump back and forth between the insect and its shadow, producing artifacts in the recorded trajectory. In some situations, the shadow cast by an insect can be larger than the insect itself. We employ a series of dilation and erosion operations to eliminate the insect’s shadow and/or merge it with its body [24, 25]. Eroding the image results in the elimination of smaller shadows, while dilation causes the insect and its shadow to merge into a single blob. Figure 3 illustrates the effect of dilation and erosion on an insect blob and its shadow. Typically, a series of erosions followed by a series of dilations are applied, or vice versa. Because the size of the shadow often depends on the time of the day and the actual size of the insect, the appropriate sequence of dilation and erosion operations must be determined empirically.
3.2.2 Insect tracking
At the beginning of this stage, the binary frame returned from the image processing pipeline is segmented using a simple contour finding algorithm , and the centroid of each detected blob is calculated. We employ a human-computer collaborative tool to accurately track the centroids of the focal insects; an automated algorithm performs basic tracking with a human operator supervising and intervening when there are tracking ambiguities. This is necessary when, for instance, the frame contains a significant level of noise as a result of quick changes in lighting conditions due to moving clouds or because of relatively strong winds, which tend to shift the camera. Figure 4 illustrates the steps involved in tracking. We describe the steps below:
Skipping to the beginning. In behavioral assays where experiments are conducted in rapid succession, a single video file may contain several experiments. The human operator may need to indicate the beginning and end of each independent experiment by fast-forwarding the video to the starting position of the experiment for instance. Additionally, some non-relevant video segments may need to be cropped (equipment or hands appearing in the beginning of the experiment, for instance).
Insect selection. When there are multiple prominent blobs in the first video frame, the focal insects may need to be identified manually in the first frame. In this case, the automated algorithm stops, and control is handed to the human operator to identify the focal insects. The operator selects one or more insects by clicking on them or fast-forwarding if no insects are present. After the selection, the control is handed back to the automated tracking algorithm. Although user identification of individual insects is not feasible when tracking a large number of insects, manual selection is often necessary in behavioral assays where the focal insects need to be accurately identified and tracked for the data to make any sense. In passive observation of large insect collectives, a different mechanism needs to be implemented to identify the initial position of insects. A simple approach is to assume that all blobs in the binary frame are potential insects. Alternatively, a more sophisticated scheme, such as pattern matching, could be employed.
Insect tracking. A region of interest (ROI) is defined as the circle around the current position of tracked insects, with a radius that is three to four times the size of the insect. The selection of the focal insect in the previous step initializes the position of the ROI. The automated algorithm then steps automatically through the video frames, associating insect blobs with their predecessor in previous frames. The algorithm only considers blobs that are inside the insect’s ROI. For each frame, one of the following three situations may arise:
No blobs are detected inside the ROI. This case happens if the insect stops moving for some time, becoming part of the background model. Since there is no movement, these frames are skipped.
Ideally, one blob would be present inside the ROI. In this case, the centroid of the blob is appended to the insect’s trajectory.
Two or more blobs detected inside the ROI. This is when the human operator needs to intervene. In this situation, using the mouse, the human operator selects the blob that corresponds to the insect. This situation can often be resolved automatically using a nearest neighbor algorithm for instance. Here, we opted to rely on human judgment to maximize the accuracy of tracking. However, in more forgiving situations, one could calculate a confidence level at every frame and stop the automated tracking only when the confidence level drops below a certain threshold (e.g., when the last position of the insect is equidistant to several blobs that are equally probable).
Trajectory correction. Once the trajectory is fully processed (i.e., the insect exits the field of view or the experiment is terminated), the human operator may delete extraneous jumps by clicking on them. The final trajectory is then saved to a file.
3.3 Camera modeling
Once trajectories are extracted, they are first corrected to cancel perspective distortion. In the lab, the experimenter can typically hang the cameras from the top - using a mounting structure - to get top-down, orthogonal shots of the insects, which reduces the amount of perspective distortion. In the field, however, it is difficult to construct such mounts. Therefore, researchers often resort to using regular tripod mounts and positioning the camera on the side of the subjects to avoid disrupting their behavior. This side-top view produces a significant amount of perspective distortion.
3.4 Visual exploration (qualitative analysis)
Once trajectories are extracted and corrected, researchers can begin their attempt to understand and characterize the underlying insect behavior. Often, the first thing researchers want to do is to ‘take a look’ at the collected trajectories to get an overall sense of the behavior and to see if there are any obvious patterns. Although entomologists tend to form their initial hypotheses from field observations, it is beneficial to give them a chance to explore and follow up on a wider range of plausible theories before drawing conclusions. This is particularly important in behavioral entomology where the underlying insect behavior is highly probabilistic and is susceptible to many different interpretations that are often equally plausible. Therefore, at this stage of the workflow, our goal is to give researchers a tool that enables them to ‘think laterally’ and explore different hypotheses with ease before deciding on the most promising ones for further statistical analysis. Supporting this kind of exploratory qualitative analysis in scientific workflows is crucial, yet often overlooked .
We included two interactive features to let researchers query the data, explore hypotheses, and quickly determine whether the data support those hypotheses. First, a coordinated paintbrush tool allows the user to ‘brush’ the background of one trajectory, causing a color highlight in all other displayed trajectories when the insect moves over a brushed area. Second, a temporal filter enables the user to specify a time period (using a range slider), causing the visualization to display trajectory segments corresponding to insect movement during the specified time period only, such as the beginning of the experiment. Our experiments with the visualization demonstrated that using these two features in tandem, a researcher could test for a hypothesized spatiotemporal behavioral pattern and visually determine whether the data support that behavior .
To see how the visualization can be used for quick qualitative hypothesis testing, let us consider the following example. During the study on the navigational strategies of seed harvester ants (described in Section 4), our field observations suggested that ants were employing celestial cues, such as polarized sunlight, for navigation off the colony's main foraging trail where no reliable pheromone cues are present. To test this hypothesis, the researcher visualized trajectories of ants captured east of the main foraging trail in one group and tried to determine whether those ants exit the experimental arena from the west side in an attempt to get back to the trail. Because of the large number of samples (over 50 in our case), this is not normally an easy task. However, the test can be visually performed with ease using our visualization; the researcher uses the coordinated paintbrush tool to brush the left (west) part of one trajectory from the ‘east’ group with red (top right of Figure 6A) and set the temporal filter to show movement during the last moments of the experiment. One would expect a red highlight in the majority of cells if the ants exit from the left side, which is indeed the case here. While this qualitative assessment does not, by itself, constitute formal verification, it can be used to contemplate and explore a wide range of hypotheses. Once a number of plausible hypotheses have been identified, they can be statistically verified in the following stage of workflow.
3.5 Quantitative analysis
Quantitative trajectory description. Trajectories are first discretized at regular intervals, in both space and time. Following that, various statistical and geometric measures are calculated from the discretized segments, including distribution of turning angles and mean orientation vectors. These measurements quantitatively characterize insect motion and allow researchers to establish quantitative differences between groups of trajectories captured under different conditions.
Statistical hypothesis testing. This usually entails comparing groups of trajectories based on the measures calculated in the above step. Statistical tests such as Wilcoxon and generalized linear model (GLM) are common here.
While the appropriate statistical test is largely dependent on the question being asked, there are a number of general statistical measures that can be used to quantitatively characterize the movement of terrestrial insects. Moreover, these measures can shed light on the strategies insects employ to process stimuli and navigate the environment around them. Although not meant as an exhaustive list of measures, here we discuss statistical and geometric trajectory measures that are widely used in the analysis of insect movement. We first discuss trajectory discretization and then describe two common statistical measures for trajectories.
3.5.1 Trajectory discretization
Since the motion of insects is highly stochastic in nature, it is convenient to chop their trajectories into regular segments and analyze those segments differentially. This is often necessary to get a statistically representative sample of the insect’s motion and decision making process. Although trajectories extracted from image sequences are often recorded as a series of discrete points, such initial discretization is usually irregular or fixed at an arbitrary interval (the video’s frame rate). One should therefore resample the trajectories at biologically meaningful distances and intervals. The choice of segment length is subject to a trade-off between incorporating too much noise by using a small segment length and sacrificing resolution by taking too large a segment length. Ultimately, that choice depends on the questions being asked and the phenotypic behavior of the insect. Common values range between few millimeters to few centimeters for space discretization and few hundred milliseconds to few seconds for time discretization.
3.5.2 Distribution of turning angles
3.5.3 Distribution of orientations
This can be calculated by determining the normalized orientation vector of the insect at every segment throughout the trajectory. Figure 8B illustrates this. Orientation vectors can be averaged to calculate a mean orientation vector, which gives the overall direction of the insect’s motion. The magnitude of the mean orientation vector indicates the directedness of an insect’s path. A mean value close to 1.0 implies that the insect is traveling in a particular direction, whereas a magnitude close to 0 typically indicates a non-directed motion, such as a loop or spiral.
There are some technical limitations in the workflow that need to be addressed before it can be adopted on a larger scale. At the moment, the workflow was implemented in separate tools using C++, Java, and Matlab. In the future, we would like to combine all the stages into a single application. The integration between qualitative and quantitative analyses would be particularly helpful. This would allow researchers to explore the dataset and visually formulate hypotheses from the visualization environment with simple interactions, with the computer translating the qualitative hypotheses into statistical tests that are performed automatically for verification.
A second concern is the significant effort a researcher needs to devote for data acquisition, compared to fully automated methods. Although human-guided image processing is well suited for studies relying on behavioral assays where insects are often manipulated and filmed individually, such an approach is less efficient in studies that rely on long passive observation of insect collectives in the field. While we still believe that a human-computer collaborative approach is promising even for observational studies with hours of video recordings, the role of the human analyst needs to be further restricted. In the current implementation, the image processing tool requires a human operator to continuously supervise and take action to resolve ambiguities and/or correct tracking errors before processing continues. To minimize the amount of supervision time, we envision a backend image processing system that is capable of automatically analyzing most of the data offline, stopping only at frames that suffer high noise levels and queuing those up for human intervention at a later time. The operator would use a frontend tool to quickly scan through the accumulated frames to visually resolve them at his/her convenience. The backend system would take the user input and continue offline processing, queuing up any additional frames that are difficult to process for human intervention.
The study of insect behavior from image sequences poses many challenges. Despite the advances in image processing techniques, the current generation of insect tracking tools is only effective in controlled lab environments and under ideal lighting conditions. In this paper, we presented an end-to-end workflow for the acquisition, processing, and analysis of the movement trajectories of terrestrial insects in the field. The workflow employs human-guided video analysis to overcome limitations in automated algorithms when faced with an unpredictable visual scene and highly dynamic lighting conditions. Our technique improves tracking precision by an average of 20% to 44% compared to traditional automated methods. The workflow also incorporates a novel trajectory visualization tool for large-scale exploratory analysis of insect movement patterns, allowing researchers to visually formulate and test hypotheses pertaining to insect behavior. Further, we provide a number of generic statistical analysis methods for the quantitative analysis of insect behavioral patterns. We demonstrated the effectiveness of the proposed techniques with a field case study that investigated the navigational strategies employed by Kenyan seed harvester ants in their native habitat.
aAn implementation of the workflow along with the source code is available at http://www.evl.uic.edu/kreda/field_entomology/.
We are grateful to Iain Couzin, Daniel Rubenstein, Tanya Berger-Wolf, and Jason Leigh for the helpful discussion in the field; to Andrew Johnson for his feedback on the visualization tool; and to Karl Li for lending us his awesome laptop. This work was part of a project performed in the joint Princeton-UIC Field Computational Ecology course in Spring 2012 (http://www.cs.uic.edu/bin/view/ComputationalEcology), with co-instructors Tanya Berger-Wolf and Jason Leigh (University of Illinois at Chicago), and Daniel Rubenstein and Iain Couzin (Princeton University), who were instrumental in several parts of this research. We thank the staff at Mpala Research Centre (Kenya) and Ol’ Pejeta Nature Conservancy (Kenya), and fellow graduate students at EEB-Princeton University, CS at University of Illinois at Chicago, and University of Nairobi. Funding was provided by the Department of Ecology and Evolutionary Biology of Princeton University: NSF OCI-1152895 ‘EAGER: Field Computational Ecology Course’ (Berger-Wolf and Rubenstein), NSF IIS-0747369 (Berger-Wolf), and NSF OCI-0943559 (Leigh).
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