 Research Article
 Open Access
Three Novell AnalogDomain Algorithms for Motion Detection in Video Surveillance
 Arnaud Verdant^{1},
 Patrick Villard^{1},
 Antoine Dupret^{2}Email author and
 Hervé Mathias^{3}
https://doi.org/10.1155/2011/698914
© Arnaud Verdant et al. 2011
 Received: 1 May 2010
 Accepted: 8 December 2010
 Published: 18 January 2011
Abstract
As to reduce processing load for video surveillance embedded systems, three lowlevel motion detection algorithms to be implemented on an analog CMOS image sensor are presented. Allowing onchip segmentation of moving targets, these algorithms are both robust and compliant to various environments while being power efficient. They feature different tradeoffs between detection performance and number of a priori choices. Detailed processing steps are presented for each of these algorithms and a comparative study is proposed with respect to some reference algorithms. Depending on the application, the best algorithm choice is then discussed.
Keywords
 Power Consumption
 Motion Detection
 Video Surveillance
 Background Estimation
 Motion Segmentation
1. Introduction
Motion detection in video surveillance with CMOS Image Sensors (CIS) requires high performance but it also needs to meet power consumption constraints, especially for remote sensing applications.
One way to address this issue is to design ASICs with specific image processing architectures. It allows some low level local analog processing to be performed at the sensor level (prior to A/D conversion), which is particularly power efficient. Thanks to submicron CMOS processes, the insensor processing can be performed without significantly impairing the device resolution and sensitivity. In the case of embedded video surveillance with a major concern on autonomy, such a physical motion detection implementation is a particularly interesting task to investigate since it allows extracting relevant information from a scene prior to broadcasting. This could be used to adapt the sensor's performance such as ADC resolution. Power consumption for capturing, storing, and transmitting the video would so be reduced. However, specific adapted algorithms have to be developed concurrently. Since such sensors have to be fully autonomous, these algorithms have to be both robust and compliant to various environments while being at the same time computationally and power efficient.
In the case of quasisteady camera (video still), adaptive environment modeling constitutes a key point in motion segmentation for surveillance systems. Among many works focusing on computer vision, the visual surveillance problem is discussed in [1], where conventional approaches for motion detection are presented. Implementation of optical flow measurement is also an interesting wellknown technique in [2, 3]. These precedent approaches focus on optimizing motion detection in CIS but are not concerned with very low power image processing. In addition, optical flow methods based on TwoFrame Differential Method (i.e., Lucas and Kanade [4] or Horn and Schunk [5]) are based on hypotheses such as illumination steadiness. Such hypotheses are not always relevant, especially when objects move fast with respect to the frame rate. The aperture problem also constitutes a limitation to their straightforward implementation. Hence, these algorithms require iterative multiresolution processing as to extract information.
On the other hand, motion detection achieved by estimating background is based on weaker hypotheses. Background updating is an essential task since realtime algorithms for embedded systems have to be efficient in a large number of situations, that is able to adapt their sensitivity to the scene. Image segmentation with difference to background and adaptive threshold has been studied in [6], where the signal variance is computed from recursive average computations and then compared to a threshold obtained by averaging background variance over all the pixels. This method has been improved in [7] where its inherent trailing effect is compensated by a confidence weight representing the confidence of a pixel being part of the foreground. Adaptive threshold for motion detection in outdoor environment has been explored in [8]. The histogram of a distant matrix (obtained with Principal Component Analysis technique) and the variance of a mean image allow adapting the threshold level according to outdoor conditions. Other approaches based on multiple background estimations [9] or adaptive background estimation [10] have also been proposed.
All the precedent methods are efficient but require many operations. Due to the reduced processing resources available in CMOS Image Sensors, computational efficiency is so required yet keeping enough robustness. In order to perform low power motion detection in CIS, other methods based on background modeling have been proposed. In [11] lowlevel motion detection algorithms are presented and in [12], an efficient algorithm based on ΣΔ modulation for artificial retinas is described. In this work, robustness improvement to false positives is achieved with local thresholding. For each pixel, background estimation and variance are computed with nonlinear operations to perform adaptive local thresholding.
In our proposed motion detection scheme for increased autonomy, such algorithms [11, 12] need to be improved in terms of false positives and detection efficiency while only using low power operations. The developed algorithms based on lowlevel computations are designed to be implemented on a versatile analog architecture allowing a wide range of operators and compact processing steps. In this paper, after a short presentation of our architectural choices and their consequences on the associated algorithms (part 2), we describe the motion detection algorithms we take as reference (part 3). We then present the developed motion detection algorithms with associated results and estimated power consumption (part 4). Finally, we discuss the algorithms performance from different points of view in order to balance purely simulated results according to targeted application.
2. Constraints and Targeted Architecture
2.1. Programmable Architecture
This architecture is implemented using a 0.35 μ m CMOS process. It features a 10 μ m pixel pitch with a standard fill factor (30%). With small parasitic capacitors and 3.3 V voltage swing, it constitutes a good compromise with respect to larger or to deep submicrometer processes. Moreover, leakages are also reduced compared to more advanced technologies, thus reducing static power consumption as well as defects in Analogue RAM (ARAM).
In order to take advantage of the SIMD architecture parallelism, the motion segmentation has to be performed independently for each pixel. The corresponding processing so requires many identical operations to be performed iteratively. Provided that the variables involved in the computations are independent, a parallel implementation of algorithms is thus possible and interesting in order to reduce the global power consumption. An analogbased computational system is an efficient response to these constraints.
With such an architecture, performing motion detection algorithms in the analog domain can be achieved with little power requirements. For example, mixing capacitors charges at pixel level [14] efficiently performs pixel averaging. A digital counterpart implementation would require numerous computations and power consuming data transfers.
 (i)
pixel average,
 (ii)
recursive average (i.e., weighted sums),
 (iii)
fixed step increments/decrements,
 (iv)
storage (state).
The most used operators are addition, multiplication of a variable by a fixed coefficient, increment, absolute value, and comparison. Conditional operations are needed, their executions depending upon comparison results referred to states.
Our analogbased architecture has been shown to overcome its digital counterparts in [15] in the context of a low power CMOS image sensor based on a waking up scheme for which the presented algorithms have been optimized.
2.2. Methodology
Concluding on algorithm performance is achieved by measuring motion detection performance on Matlab, as well as induced power consumption and temporal noise effect of CMOS devices using a SystemC model of the system (architecture and algorithm).
2.3. Metrics Choice and Performance Evaluation
Performance metrics are based on [16]. During the simulation, motion segmentation is performed on gray level images resulting in binary images containing "moving" and "static" pixels. Each image is then divided in blocks of pixels. If a block contains more than a predefined number of moving pixels, this block is then considered as a region of interest (ROI). From experimental evaluations based on a hand generated ground truth, an ROI can be considered as active when 5 to 10% of the pixels are "moving". Measurements for reference algorithms as well as proposed new ones are based on this value. For each frame, the state of each block is stored in a vector. This vector is compared to a reference which indicates ground truth information for the current frame. The number of True Positives and False Positives and Negatives can thus be counted ( , , , ).
 (i)
Detection Rate ( ), which is the ability of the algorithm to detect moving objects,
 (ii)
False Alarm Rate ( ) which estimates detection quality,
 (iii)
False Positive Rate ( ), which is representative of algorithm robustness.
In our sequence, nonrelevant motion concerns static elements of the scene or other elements such as snow in dtneu_schnee sequence, rustling foliage in Walk and kwbB sequences and strobe light in Pets 2002 sequence.
We have developed a faithful, Cycle Accurate, SystemC behavioral model of the architecture [17]. This model enables to jointly simulate the proposed algorithms and the processing architecture. This SystemC modeling is used to determine the number of instructions and the instruction rate required for each algorithm. The SystemC modeling also enables checking the consistency between the results obtained by the model and purely algorithmic results. A log file allows tracing instructions and data, hence enabling to check the whole coherence of the architecture for any conflicts during the parallel processing.
In order to take into account the impact of the nonidealities introduced by the analog parts and to get an accurate evaluation of power consumption, the analog blocks composing the architecture have been described at a low level, down to simple components like switches, capacitors, OTAs. For all these elementary blocks, relevant nonidealities have been modeled with respect to the target CMOS technology and validated thanks to classical electrical simulations (Spicelike). The power consumptions given in the next parts derive from this SystemC modeling of our architecture. Some hints about these aspects of the works have been exposed in [17].
3. Starting Point: and Algorithms
The embedded power motion detection algorithms have to meet two requirements: limited complexity, as to comply with our CIS computational limitations and high performance. In order to perform adaptive motion detection, background modeling has been chosen because of its computationally efficient implementation. In [11], two techniques allowing adaptive background modeling are presented. These algorithms perform local computations (i.e., from each pixel value) in order to generate low pass filtering on the observed scene. Approaches based on connectedcomponent extraction, object merging, clustering are not explored here, because they require too intensive calculations with regard to the aimed architecture.
3.1. Background Estimation Using and Recursive Average Algorithms
The autonomous remote CIS we develop must perform motion detection in unknown and potentially changing environments. In such configurations, algorithms must meet hard constraints of robustness and adaptability. Markovian algorithms are generally used to face these situations. However, with respect to the considered power consumption and computational constraints, we had to simplify algorithms of this class while preserving their robustness.
As reference algorithms, we consider the Recursive Average ( ) algorithm and the algorithm, respectively, presented in [11, 12]. Both feature simple arithmetic computations. Moreover, the algorithm, which follows the Markov model and has been used for realtime implementations in [18, 19], provides high robustness.
3.1.1. Recursive Average: Principle
Motion detection performance of two stateoftheart algorithms.
Grey level sequence  Performance metrics (%)  

Detection Rate (DR)  Hall  97.3  94.2 
kwbB  97.8  94.6  
Walk  100  99.1  
Pets 2002  95.8  93.3  
dtneu_schnee  99.9  91.6  
False Alarm Rate (FAR)  Hall  79.3  16.3 
kwbB  81.7  32.4  
Walk  84.8  86.7  
Pets 2002  85.0  28.3  
dtneu_schnee  54.8  43.7  
False Positive Rate (FPR)  Hall  42.0  2.5 
kwbB  15.4  2.7  
Walk  59.2  60.5  
Pets 2002  16.5  1.6  
dtneu_schnee  24.3  14.5 
3.1.2. : Principle
Considering an analogue implementation, the main advantage of this method is that it features more flexibility than the algorithm. Indeed, estimated background variations can be adjusted by incrementation/decrementation steps, whereas time constant values of recursive averages are limited by the physical implementation of the computation. In our architecture, these time constant values are fixed by the ratios of the capacitances on which the signals charges are shared.
Instead of the global threshold used in , the algorithm so computes a local adaptive threshold for each pixel as to achieve more robustness on noisy elements, while keeping enough sensitivity on static background. Thanks to the observed scene nonuniformity, local thresholding is computed according to the temporal activity of each zone. Moreover, this algorithm features no trailing effects, at the cost of a poor band pass filtering capability.
3.1.3. Recursive Average and Performance
Table 1 presents the motion performance of stateoftheart algorithms. The value used for the algorithm is 2^{5}. The value used for the algorithm (required for threshold processing) is 15.
exhibits poor robustness. Indeed, this algorithm requires setting a global threshold that constitutes the main limitation of this method since no sensitivity adaptation according to scene activity can be performed. Moreover, exhibits phase shifting resulting in trailing effects and poor band pass filtering. More specifically, this algorithm does not allow high frequency rejection along with background subtraction.
The motion detection performance exposed for the algorithm clearly shows the interest of local adaptive thresholding compared to the global one used by the algorithm.
However, the onchip motion detection information can be used to adapt the sensor performance (e.g., higher ADC accuracy on moving pixels). In order to keep a reasonable global power consumption (a few mW), an improved robustness of these onchip motion detection analog domain algorithms is still required while keeping high detection rate.
4. Algorithms
 (i)
a first algorithm running with no a priori determination of constant, based on scene activity to adapt its sensitivity,
 (ii)
a second algorithm using band pass filtering in order to reduce false positives upon high frequency pixel variations,
 (iii)
finally, an algorithm featuring only one constant to determine a priori, and reducing the trailing effect induced by recursive averaging.
4.1. SceneBased Adaptive Algorithm (SBA)
In order to improve adaptability, we now present the SceneBased Adaptive (SBA) algorithm. This algorithm derives from the algorithm in [12]. It performs motion segmentation on gray level sequences with no a priori constant determination, like the N constant used in . Based on modulations, the SBA algorithm is also compliant with the reduced available computational resources of CIS architectures, thus eliminating true Markovian approaches.
The value generated is now used to perform adaptive motion detection with the technique presented below.
First, the mean value of is computed (7). Considering that insignificant motions of the background introduce only small variations changes, the idea is to favor large signal variations at the expense of small ones. A convex function is so needed to amplify . Therefore, (8) introduces which is an approximation of . Indeed, our switched capacitor architecture enables only multiplication between a digital number (i.e., the steps of ) and an analog value (i.e., ).
The absolute difference between and can be seen as the maximal estimated signal dispersion. A larger variation than the estimated one is considered due to a relevant moving object (10). Apart from the increment or decrement level, this algorithm runs without any a priori fixed constant.
4.2. Recursive Average with Estimator Algorithm (RAE)
In various outdoor situations, many false alarm sources can be encountered. Despite the fact that the static background encountered in urban area does not provide such constraints, weather conditions in the same areas can lead to increased FPR and FAR. In [12], no high frequency rejection is performed, thus implying numerous false positives.
Figure 12(b) illustrates motion detection, performed at a crossroad under falling snow, with the algorithm. In order to improve motion detection robustness by rejecting high frequency variations, we have designed an algorithm featuring band pass filtering. It is also based on recursive average which can be compactly implemented considering charge transfer between capacitances. Though having the same degree of complexity, the designed algorithm is thus optimized for an analogbased architecture, compared to delta modulation.
4.3. Recursive Average with Estimator Algorithm (RAE)
In various outdoor situations, many false alarm sources can be encountered. Despite the fact that the static background encountered in urban area does not provide such constraints, weather conditions in the same areas can lead to increased FPR and FAR. In [12], no high frequency rejection is performed, thus implying numerous false positives.
Figure 12(b) illustrates motion detection, performed at a crossroad under falling snow, with the algorithm. In order to improve motion detection robustness by rejecting high frequency variations, we have designed an algorithm featuring band pass filtering. It is also based on recursive average which can be compactly implemented considering charge transfer between capacitances. Though having the same degree of complexity, the designed algorithm is thus optimized for an analogbased architecture, compared to delta modulation.
The constant has been set to 2^{6} ( or 200 frames). The constant can be typically set around 2 and can be increased in order to reduce false positives.
Figure 11 illustrates computations of a pixel signal using the proposed algorithm.
One can notice that this algorithm can bring efficient filtering of high frequency perturbations. However, some trailing effect is observed with the RAE algorithm (not obtained with ). Figure 12 illustrates RAE applied on the dtneu_schnee sequence with falling snow. With the same sensitivity as , this algorithm allows to filter these high frequency perturbations.
4.4. Adaptive Wrapping Thresholding Algorithm (AWT)
Although being robust and computationally efficient, the and RAE algorithms require determining some constants. According to the known frame rate, the , , and constants of RAE as well as the increment level of can be determined a priori. However, the RAE constant or the constant allows adjusting the algorithm sensitivity in accordance with the amplitude of noisy elements. In order to avoid defining a priori constants, an Adaptive Wrapping Thresholding motion detection algorithm (AWT), based on recursive average operations with a reduced number of constants, is presented in this section. Unlike common algorithms based on recursive low pass filtering [6], this algorithm also limits the trailing effect due to phase shifting.
This algorithm relies on a background estimation for each pixel signal from which we estimate the signal standard deviation. This standard deviation is then used to estimate a maximum range for background variations. If the value of a considered pixel moves outside this estimated range of background variations, we consider that motion occurs.
Hence this algorithm relies on a constant, , allowing to determine the time constant of recursive averages (equivalent to increment/decrement levels of the algorithm [12]). However, no additional constant is required to handle sensitivity, unlike or RAE where a coefficient is required to set the threshold level. Computations of and allow here to define adaptive thresholding directly from the signal variations (Figure 13).
Unlike , SBA or RAE, there is no need for a multiplication operation. From our analog implementation point of view, this constitutes an improvement since there is no need to implement multiple capacitors to get a wide range of constants for multiplication.
5. Results
5.1. Algorithms Performance
Motion detection performance.
Grey level sequence  Performance metrics (%)  

SBA  RAE  AWT  
Detection Rate (DR)  Hall  97.3  94.2  93.5  94.8  92.8 
kwbB  97.8  94.6  94  96.4  96.6  
Walk  100  99.1  99.3  99.5  99.3  
Pets 2002  95.8  93.3  94.1  93  94.6  
dtneu_schnee  99.9  91.6  90.1  87.5  90.1  
False Alarm Rate (FAR)  Hall  79.3  16.3  14.9  12.6  16.7 
kwbB  81.7  32.4  27.4  26.4  36.8  
Walk  84.8  86.7  83.4  85.7  85  
Pets 2002  85.0  28.3  43.4  26.2  29.8  
dtneu_schnee  54.8  43.7  54.9  11.9  45.2  
False Positive Rate (FPR)  Hall  42.0  2.5  2.2  1.8  2.5 
kwbB  15.4  2.7  1.7  1.7  3.0  
Walk  59.2  60.5  46.7  56  52.9  
Pets 2002  16.5  1.6  3.9  1.2  1.6  
dtneu_schnee  24.3  14.5  22.1  1.8  13.3  
Number of Instructions  6  30  43  21  32 
Motion detection performance.
Algorithm  Average parameter variation on 5 sequences (%)  

DR  FAR  FPR  
 —  —  — 
 −0.9  50.8  161.3 
SBA  −13.3  9.2  −11.6 
RAE  0.7  4.7  8.9 
AWT  −0.2  −4.4  −8.3 
Average motion detection performance.
Algorithm  Performance metrics (%)  

FAR  DR  FPR  
 41.5  94.6  16.3 
SBA  44.8  94.2  15.3 
RAE  32.6  94.2  12.5 
AWT  42.7  94.7  14.6 
The AWT algorithm results are slightly below the performance levels of RAE. However, no a priori choice of threshold sensitivity has been made. Hence these results highlight interesting performance about motion detection without environment knowledge.
TheWalk sequence denotes reduced robustness here. Although rustling foliage is efficiently filtered out by our algorithms, the motion of the tree branches has the same speed and amplitude characteristics as the objects to be detected (e.g., humans). The single processing is not robust to such motion.
The power consumption is proportional to the Number of Instructions (NOI). From SystemC simulations applied to 30 fps video sequences, we have estimated a power consumption below 5 mW for the worst case (SBA algorithm). This is less than the power consumption of a state of the art 3 M samples/s 10bit Successive Approximation Register (SAR) ADC designed in the same technology, that is between 10 and 20 mW. The SAR are known to be the least power consuming ADC architectures. This validates the relevance of the algorithm architecture codesign since a digital implementation of those algorithms would require such an ADC plus a digital processing unit. Furthermore, our analog processing unit derives from a SAR ADC; therefore, the scaling of the CMOS technology brings the same improvements as for the classical SAR ADC.
So as to take into account technological parameters in these simulations, temporal noise had been added in these sequences via our SystemC model. Indeed, in our architecture, several noise sources create signal variations that can be interpreted as relevant motion. In our model, the 8bit images are converted into voltage signal on a 1.8 V dynamic range. An additional Gaussian noise with a 1.1 mV standard deviation is added to each image. During processing, a second Gaussian noise source with a 0.25 mV standard deviation is added to each operation to model analog processor nonidealities.
Table 3 presents the impact of noise on analog processing on the different motion detection parameters considered.
We can see that in the case of SBA and , DR is reduced while FAR is increased. For these two algorithms, noise induces less sensitivity on relevant part of the scene, while decreasing global robustness. These results highlight the lower robustness of these two algorithms when implemented in our analog architecture. Concerning the RAE algorithm, both DR and FAR are amplified. This can be due to an insufficient threshold amplification. For AWT algorithm, the whole parameters are decreased. The threshold amplification is too high for this one, leading to less sensitivity on the whole images. However, the noise added on recursive averagebased processing (RAE, AWT) induces fewer variations for the selected parameters. Thus we can consider that the recursive averagebased methods are more robust than the ones based on modulations ( , SBA), when implemented in our analog architecture.
5.2. Discussion
In the precedent part, we have presented 3 robust and fast new algorithms and compared them to the reference algorithm. Based on particular parameters allowing the measurement of motion detection performance, such as detection rate or false positive rate, we have determined the robustness or detection efficiency of these algorithms. The average results for the tested sequences are presented on Table 4.
 (1)
settings: the fewer the required constants for adapting threshold level or time constants, the more autonomous the leftbehind sensor,
 (2)
adaptation: threshold level evolution according to pixel temporal activity,
 (3)
high frequency rejection: high frequency noise filtering of pixel signal (band pass filtering),
 (4)
trailing effect: artefacts or motion segmentation distortion due to phase shifting induced by algorithm,
 (5)
robustness: number of generated false positives,
 (6)
computational efficiency: induced power consumption (mainly depending on the number of instructions in our implementation),
 (7)
robustness with regard to analog implementation (temporal noise).
Balanced algorithm performance according to selected criteria.
Algo.  Criteria  

1  2  3  4  5  6  7  
 − 
 −  − 
 + +  
 −  +  −  +  ±  + 

SBA  +  +  −  −  +  −  − 
RAE  −  +  + +  −  +  +  + 
AWT  +  +  +  +  ±  −  + 
These qualitative results show that, depending on the aimed application, an algorithm can prevail on another, even if its motion detection performance is worse. However, AWT and RAE are better suited for an analog implementation.
6. Conclusion
Three algorithms developed using a codesign approach have been presented. They perform motion detection at reduced power consumption while ensuring fast and robust computation. Compared to classical sensors performing motion detection downstream the image acquisition, the offered processing capabilities are somehow limited, but the chosen analog architecture, on which they are implemented, offers a better compromise between power consumption and algorithm performance. Moreover, considering only the algorithmic aspect of the works, significant improvements have been brought in terms of selfadaptability to the scene. Constants involved in the presented algorithms are indeed mostly depending on the nature of the objects to be detected (speed and size).
Though these algorithms have been tailored for a dedicated architecture, a realtime implementation on a standard digital processor (e.g., an ARM920T) is however possible but at a significantly higher power consumption (roughly some 100 mW for the processor alone).
Finally, an ASIC is currently being designed as to provide an experimental validation of the concept. One of its main features is that the pixel area ( μ m^{2}) is very close to stateoftheart pixels in similar technology (0.35 μ m CMOS).
Authors’ Affiliations
References
 Hu W, Tan T, Wang L, Maybank S: A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man and Cybernetics Part C 2004, 34(3):334352. 10.1109/TSMCC.2004.829274View ArticleGoogle Scholar
 Moini A, Bouzerdoum A, Eshraghian K, Yakovleff A, Nguyen XT, Blanksby A, Beare R, Abbott D, Bogner RE: An insect visionbased motion detection chip. IEEE Journal of SolidState Circuits 1997, 32(2):279284. 10.1109/4.551924View ArticleGoogle Scholar
 Mehta S, EtienneCummings R: Normal optical flow measurement on a CMOS APS imager. Proceedings of the IEEE International Symposium on Cirquits and Systems (ISCAS '04), May 2004 4: 848851.Google Scholar
 Lucas BD, Kanade T: An iterative image registration technique with an application to stereo vision. Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI '81), April 1981 674679.Google Scholar
 Horn BKP, Schunck BG: Determining optical flow. Artificial Intelligence 1981, 17(13):185203. 10.1016/00043702(81)900242View ArticleGoogle Scholar
 Joo S, Zheng Q: A temporal variancebased moving target detector. Proceedings of the IEEE Workshop on Performance Analysis of Video Surveillance and Tracking (PETS '05), January 2005Google Scholar
 Abdelkader MF, Chellappa R, Zheng Q, Chan AL: Integrated motion detection and tracking for visual surveillance. Proceedings of the 4th IEEE International Conference on Computer Vision Systems (ICVS '06), January 2006 28.View ArticleGoogle Scholar
 Vázquez JF, Mazo M, Lázaro JL, Luna CA, Ureña J, Garcia JJ, Guillan E: Adaptive threshold for motion detection in outdoor environment using computer vision. Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE '05), June 2005 3: 12331237.View ArticleGoogle Scholar
 Pan W, Wu K, Chai Z, You ZS: A background reconstruction method based on doublebackground. Proceedings of the 4th International Conference on Image and Graphics (ICIG '07), August 2007 502507.View ArticleGoogle Scholar
 Guo J, Rajan D, Chng ES: Motion detection with adaptive background and dynamic thresholds. Proceedings of the 5th International Conference on Information, Communications and Signal Processing, December 2005 4145.Google Scholar
 Richefeu J, Manzanera A: Motion detection with smart sensor. Proceedings of the 9th Congress Young Searchers in Computer Vision (ORASIS '05), May 2005Google Scholar
 Manzanera A, Richefeu JC: A new motion detection algorithm based on ΣΔ background estimation. Pattern Recognition Letters 2007, 28(3):320328. 10.1016/j.patrec.2006.04.007View ArticleGoogle Scholar
 Moutault S, Mathias H, Klein JO, Dupret A: An improved analog computation cell for Paris II, a programmable vision chip. Proceedings of the IEEE International Symposium on Cirquits and Systems (ISCAS '04), May 2004 453456.Google Scholar
 Massie M, Baxter C, Curzan JP, McCarley P, EtienneCummings R: Vision chip for navigating and controlling micro unmanned aerial vehicles. Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS '03), May 2003 3: 786789.Google Scholar
 Verdant A, Dupret A, Mathias H, Villard P, Lacassagne L: Adaptive multiresolution for low power CMOS image sensor. Proceedings of the 14th IEEE International Conference on Image Processing (ICIP '06), SeptemberOctober 2007, San Antonio, Tex, USA 5: 185188.Google Scholar
 Black J, Ellis TJ, Rosin P: A novel method for video tracking performance evaluation. Proceedings of the IEEE Workshop on Performance Analysis of Video Surveillance and Tracking (PETS '03), October 2003 125132.Google Scholar
 Verdant A, Villard P, Dupret A, Mathias H: SystemC validation of a low power analog CMOS image sensor architecture. Proceedings of the IEEE NorthEast Workshop on Circuits and Systems (NEWCAS '07), August 2007 903906.Google Scholar
 Lacassagne L, Milgram M, Garda P: Motion detection, labeling, data association and tracking, in realtime on RISC computer. Proceedings of International Conference on Image Analysis and Processing (ICIP '99), 1999, Venice, Italy 520525.Google Scholar
 Denoulet J, Mostafaoui G, Lacassagne L, Mérigot A: Implementing motion Markov detection on general purpose processor and associative mesh. Proceedings of the 7th International Workshop on Computer Architecture for Machine Perception (CAMP '05), July 2005, Palermo, Italy 288293.View ArticleGoogle Scholar
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