The use of contextual information can significantly aid scene understanding of surveillance video. Just detecting people and tracking them does not provide sufficient information to detect situations that require operator attention. We propose a proof-of-concept system that uses several sources of contextual information to improve scene understanding in surveillance video. The focus is on two scenarios that represent common video surveillance situations, parking lot surveillance and crowd monitoring. In the first scenario, a pan–tilt–zoom (PTZ) camera tracking system is developed for parking lot surveillance. Context is provided by the traffic sign recognition system to localize regular and handicapped parking spot signs as well as license plates. The PTZ algorithm has the ability to selectively detect and track persons based on scene context. In the second scenario, a group analysis algorithm is introduced to detect groups of people. Contextual information is provided by traffic sign recognition and region labeling algorithms and exploited for behavior understanding. In both scenarios, decision engines are used to interpret and classify the output of the subsystems and if necessary raise operator alerts. We show that using context information enables the automated analysis of complicated scenarios that were previously not possible using conventional moving object classification techniques.
This paper proposes two novel motion-vector based techniques for target detection and target tracking in surveillance
videos. The algorithms are designed to operate on a resource-constrained device, such as a surveillance
camera, and to reuse the motion vectors generated by the video encoder. The first novel algorithm for target
detection uses motion vectors to construct a consistent motion mask, which is combined with a simple
background segmentation technique to obtain a segmentation mask. The second proposed algorithm aims at
multi-target tracking and uses motion vectors to assign blocks to targets employing five features. The weights
of these features are adapted based on the interaction between targets. These algorithms are combined in one
complete analysis application. The performance of this application for target detection has been evaluated for
the i-LIDS sterile zone dataset and achieves an F1-score of 0.40-0.69. The performance of the analysis algorithm
for multi-target tracking has been evaluated using the CAVIAR dataset and achieves an MOTP of around 9.7
and MOTA of 0.17-0.25. On a selection of targets in videos from other datasets, the achieved MOTP and MOTA
are 8.8-10.5 and 0.32-0.49 respectively. The execution time on a PC-based platform is 36 ms. This includes the
20 ms for generating motion vectors, which are also required by the video encoder.
In this paper, we explore the complexity-performance trade-offs for camera surveillance applications. For this
purpose, we propose a Scalable Video Codec (SVC), based on wavelet transformation in which we have adopted a
t+2D architecture. Complexity is adjusted by adapting the configuration of the lifting-based motion-compensated
temporal filtering (MCTF). We discuss various configurations and have found an SVC that has a scalable complexity
and performance, enabling embedded applications. The paper discusses the trade-off of coder complexity,
e.g. motion-compensation stages, compression efficiency and end-to-end delay of the video coding chain. Our
SVC has a lower complexity than H.264 SVC, but the quality performance at full resolution is close to H.264
SVC (within 1 dB for surveillance type video at 4CIF, 60Hz) and at lower resolutions sufficient for our video
surveillance application.
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