- Open Access
Moving object detection using keypoints reference model
© Wan Zaki et al; licensee Springer. 2011
- Received: 3 March 2011
- Accepted: 3 October 2011
- Published: 3 October 2011
This article presents a new method for background subtraction (BGS) and object detection for a real-time video application using a combination of frame differencing and a scale-invariant feature detector. This method takes the benefits of background modelling and the invariant feature detector to improve the accuracy in various environments. The proposed method consists of three main modules, namely, modelling, matching and subtraction modules. The comparison study of the proposed method with a popular Gaussian mixture model proved that the improvement in correct classification can be increased up to 98% with a reduction of false negative and true positive rates. Beside that the proposed method has shown great potential to overcome the drawback of the traditional BGS in handling challenges like shadow effect and lighting fluctuation.
- Gaussian Mixture Model
- Illumination Change
- Shadow Effect
- Move Object Detection
- Lighting Difference
Today, every state-of-the-art security system must include smart video systems that act as remote eyes and ensure the security and safety of the environment. One of the main challenges in any visual surveillance systems is to identify objects of interest from the background. Background subtraction (BGS) is the most widely used technique for object detection in real-time video application [1, 2].
There are various approaches in BGS modelling. Running Gaussian average (RGA) , Gaussian mixture model (GMM) [4, 5], kernel density estimation  and median filtering [7, 8] are the most common methods due to their reasonable accuracy and speed. Although all these techniques work moderately well under simple conditions, because they treat each pixel independently without considering its neighbouring area, their performance depends strongly on environmental variation like illumination change.
Recently, affine region detectors have been used in quite varied applications that deal with extracting the natural features of objects. These detectors identify similar regions in different images regardless of their scaling, rotation or illumination. In this article, we propose a new method by combining the affine detector with a simple BGS model to detect moving-object for real-time video surveillance.
The rest of this article is organized as follows: Section 2 reviews some previous work on BGS and affine region detectors; Section 3 describes our approaches for keypoint modelling; Section 4 compares GMM with our proposed model and discusses the final result; and, finally, Section 5 concludes and provides recommendations based on the results.
2.1 Background subtracting methods
where μ is mean and α is variance and F is pixel at time t.
In Equation 2, η(u; μi, ti, t σi, t) is the i th Gaussian component, σi, tis the standard deviation and ωi, tis the weight of each distribution and u is the distribution model. The parameter K is the number of the distribution.
2.2 Scale invariant feature detectors
Regarding scale-invariant feature detectors, recently there have been several approaches proposed in various pieces of literature, but undoubtedly, most of today's state-of-the-art detectors rely on the Harris detector, which was introduced by Harris and Stephens . As an enhanced feature detector, the popular scale-invariant feature transform (SIFT) algorithm  combines the Harris operator with a staged filtering approach to extract the scale-invariant feature. The scale-invariant feature is constant with respect to image translation, scaling and rotation, and partially invariant to illumination. The main drawback of SIFT is that it suffers from high computational time. Two related methods, the Hessian-affine detector and the Harris-affine detector, were proposed by Mikolajczyk et al. [13, 14], and are another well-known set of algorithms that rely on the Harris measurement. As a matter of fact, the Hessian- and the Harris-affine detectors are identical in most cases because both detect points of interest in scale-space and use Laplacian operators for scale selection. In addition, they use the second moment of the matrix for describing the local image structure.
The second moment matrix describes the gradient distribution on a local neighbourhood of each feature and the eigenvalues of this matrix represent the signal changes neighbouring the point. Therefore, the extracted points are more stable in arbitrary lighting changes and pixel variations.
Another common technique is the speed up robust feature (SURF) , which is inspired by the SIFT detector and is based on the determinant Hessian matrix.
Features from an image which is independent from scale, rotation and lighting invariants in the scene extracted from information around a keypoint are called descriptors. Once the keypoint is found, neighbouring information of the keypoint can be extracted to uniquely identify each keypoint with respect to the local image patch. These descriptors are highly distinctive, and they are resistant to illumination change and pixel variation. Basically, the descriptors show how do the intensities are distributed around the neighbouring of each keypoint. The SIFT and SURF are two well-known methods used for extracting the descriptors. In the SIFT, local image gradients are measured at different selected scales in a region around each keypoint to extract the descriptors .
The SURF uses a similar approach to the SIFT, but instead of gradients, integral image with Haar wavelets filter are used. It is to speed up the extraction time and improve its robustness. The Haar wavelets act as simple filters to find gradient in the x and y directions, as illustrated in Figure 1a. On the other hand, the integral image will significantly decrease computational time of the gradients in which only four memory accesses and four summation operations involve (Figure 1b).
To determine the orientation of each feature, the Haar wavelets responses within certain radius area of each keypoints are calculated. Then, x and y responses of each area are summed to form a new vector in which the longest vector will show the orientation of each interest point. The descriptor components are extracted based on a square window built around the interest point. This window is then divided into 4 × 4 sub-windows in which each sub-window has four features from Haar wavelet calculated as dx, dy , |dx| and |dy|. In total, 64 features 4 × 4 × 4 are extracted for each keypoint where each feature is invariant to rotation, scale, brightness.
As one of the states-of-art scale-invariant feature detectors, the CenSurE is chosen for matching correspondence between two images of the same scene. The CenSurE computes a simplified box filtering using integral images, as illustrated in Figure 1, at all locations with different scale-spaces. The scale-space is a continuous function which is used to find extrema across all possible scales. To achieve real-time performance, the CenSurE performs the scale-space by applying bi-level kernels to the original image.
The first stage of this system deals with setting the background in the scene which is similar to all other BGS techniques. Unlike traditional background modelling, which deals with all the pixels in the frame without considering their neighbouring pixel, only the selected area of the keypoints of interest and their neighbouring pixels are considered in this system. The general flow diagram of the proposed model is as shown in Figure 3.
In median filtering, the correct selections of the buffer size n and frame time rate Δt are critical issues that affect the performance of median filtering. It has been shown by Cucchiara et al.  that with proper selection of the observation time window (n Δt), median filtering gives the best overall performance for real-time application as compared to mean and mode filtering.
After building the reference background, we need to extract a significant keypoint from the reference image. To achieve this goal, the CenSurE detector is applied to both backgrounds as well as the incoming frame to extract a reference keypoint Kr and frame keypoint Kf as shown by module 1 in Figure 3. Because the keypoint itself is not efficient enough to give us information about the scene and the lighting condition, SURF descriptors have to be extracted to gain a more stable and recognisable point.
After going through the procedure of module 2 in Figure 3, there are still some false blobs coming from the matching module. Thus, for each blob, a local thresholding method is applied to remove them using certain threshold values. For this experimental study, the threshold values are manually set and they are greyscale values varying between 40 and 50.
The local thresholding is one of the techniques which can be useful particularly when the scene illumination varies locally over time . In modules 2 and 3, the interest's pixels and their neighbouring areas are masked so that vast amount of pixel intensity from each frame can be automatically eliminated. Correspondingly, using global thresholding over this mask, we can obtain the same result as the local thresholding.
In this article, we have proposed a new method for moving object detection using a keypoint model and compared it to the GMM [1, 2, 5, 10], which is considered to be one of the best BGS models available. The Intel (R) core (TM) i7-960 @ 3.2 GHz CPU with 5 GB RAM is chosen as the hardware platform. Algorithm implementation is done using a C-based computer vision library, "OPENCV," to carry out real-time performance for these two models.
FP and FN rate for three selected databases
GMM uses weighted Gaussian distributions of pixels over sequences of a frame. Therefore, it is not able to properly handle the condition where unwanted noise abides in the scene for a long period of time. This drawback can be seen from the shadow effect database where the shadow stays in the video for a long period of time. As a result, the FN rate of GMM became twice as larger than FN rate of the keypoint model.
In the last case, both the algorithms were tested under different lighting conditions, and there are once more big FP rate differences between these two methods with a value of 0.001700 for the keypoint model and 0.019453 for the GMM algorithm. The reason behind this improvement can be found in the thresholding technique used by the keypoint model, which treats each blob independently from the others and adjusts the thresholding parameter with respect to the intensity value of each individual blob.
The graphs from Figure 6a-c illustrate the PCC for a waving tree, shadow effect and lighting difference consecutively. As these graphs show, the proposed model gives accuracy improvements in all three cases with 99.2% in shadow effect, 99.4% in waving tree and 99.5% in lighting difference.
In addition, as the graph in Figure 6 shows, the keypoint model gives a more stable performance in comparison with GMM, with less variation in PCC rate.
From Figure 5, it can be observed that qualitatively the GMM gives comparable or slightly better pixel recognition results. However, in some cases that the pixels are not compact, the object recognition or tracking is not good as our proposed keypoint model.
Table 2 presents the computational comparison of the keypoint model and the GMM, in which the proposed model gives better computational speed. For the first two cases (waving tree and shadow effect) and the last case (lighting difference) respectively, the keypoint models are 1.8 and 3.5 faster than the GMM.
Computational comparison between keypoint model and GMM
Computational time frame per second (fps)
Number of frame
640 × 512
384 × 288
640 × 512
Speed of the keypoint model is dependent on the number of keypoints recognized in the scene and is not based on individual pixels. Thus, the data from Table 2 prove that the keypoint model gives more variant computational speed in different cases due to the nature of this algorithm.
In this article, we have presented a keypoint reference model for object detection under various conditions. For the purpose of comparison, we investigated the proposed method with the well-known GMM in three challenging situations: pixel variation, illumination changes and a shadow effect. The overall evaluation shows that the keypoint modelling gives higher accuracy in all the different situations because of the reduction of TP and FN error rates.
This improvement is achieved by two main factors. First, through the use of keypoint model that considers the pixel dependency in the modelling stage. Hence, it is less sensitive to illumination changes and shadow effects. Second is due to the fact that the individual blob thresholding technique used by the keypoint model significantly helps reduce the FP rate in the final stage. The fastest and more accurate model can be gained by combining the newest matching technique and faster descriptor extractor with that in a specific environment. In addition, machine learning can be used to improve the matching accuracy.
This research was supported in part by the eScience Fund grant 01-01-02-SF0563 by MOSTI and OUP-UKM-ICT-36-184/2010 grant from the Centre for Research & Innovation Management (CRIM) of Universiti Kebangsaan Malaysia (UKM).
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