Critical video quality for distributed automated video surveillance

Abstract

Large-scale distributed video surveillance systems pose new scalability challenges. Due to the large number of video sources in such systems, the amount of bandwidth required to transmit video streams for monitoring often strains the capability of the network. On the other hand, large-scale surveillance systems often rely on computer vision algorithms to automate surveillance tasks. We observe that these surveillance tasks present an opportunity for trade-off between the accuracy of the tasks and the bit rate of the video being sent. This paper shows that there exists a sweet spot, which we term critical video quality that can be used to reduce video bit rate without significantly affecting the accuracy of the surveillance tasks. We demonstrate this point by running extensive experiments on standard face detection and face tracking algorithms. Our experiments show that face detection works equally well even if the quality of compression is significantly reduced, and face tracking still works even if the frame rate is reduced to 6 frames per second. We further develop a prototype video surveillance system to demonstrate this idea. Our evaluation shows that we can achieve up to 29 times reduction in video bit rate when detecting faces and 16 times reduction when tracking faces. This paper also proposes a formal rate-accuracy optimization framework which can be used to determine appropriate encoding parameters in distributed video surveillance systems that are subjected to either bandwidth constraints or accuracy constraints.

Video quality for face detection, recognition, and tracking

“…Also, these algorithms are sensitive to factors such as face size, lighting, background conditions, etc. For more details refer to our earlier study [Korshunov and Ooi 2005], where additional experimental results supporting this reasoning are presented.…”

“…This reduction, however, does not directly apply to a normal video, since video encoders typically use motion estimation between frames to achieve higher compression. More details about the experimental results with streaming video can be found in our previous work [Korshunov and Ooi 2005].…”

“…Therefore, thresholds and pruning values obtained in the training stage have a strong affect on algorithms' accuracy as well as their robustness to reduction in video quality. We have previously discussed in more details the effect of Viola-Jones algorithm's thresholds on its response to JPEG compression [Korshunov and Ooi 2005].…”

Scalable Video Transmission for a Surveillance System

“…They typically utilize a centralized architecture and posit the required system resources. [10] Digital surveillance systems disclose restrictions regarding delay and visual quality that pose demands on the video codec. Flexible composition of the compressed video data is required.…”

Reducing Frame Rate for Object Tracking

“…Previously, we have shown in [1] that frame rate can be significantly reduced without object tracking losing the object. We found that the critical frame rate for a given algorithm depends on the speed of tracked object.…”