Tracking-Optimized Quantization for H.264 Compression in Transportation Video Surveillance Applications

We propose a tracking-aware system that removes video components of low tracking interest and optimizes the quantization during compression of frequency coefficients, particularly those that most influence trackers, significantly reducing bitrate while maintaining comparable tracking accuracy. We utilize tracking accuracy as our compression criterion in lieu of mean squared error metrics. The process of optimizing quantization tables suitable for automated tracking can be executed online or offline. The online implementation initializes the encoding procedure for a specific scene, but introduces delay. On the other hand, the offline procedure produces globally optimum quantization tables where the optimization occurs for a collection of video sequences. Our proposed system is designed with low processing power and memory requirements in mind, and as such can be deployed on remote nodes. Using H.264/AVC video coding and a commonly used state-of-the-art tracker we show that while maintaining comparable tracking accuracy our system allows for over 50% bitrate savings on top of existing savings from previous work

Channel protection for H.264 compression in transportation video surveillance applications

The compression of video and subsequent partial loss of the compressed bitstream can dramatically reduce the accuracy of automated tracking algorithms. This is problematic for centralized applications such as transportation surveillance systems, where remotely captured and compressed video is transmitted over lossy wireless links to a central location for tracking. We propose a low-complexity method for protecting compressed video against channel loss such that the tracking accuracy of decoded and concealed video is maximized. Our algorithm leverages a previous method of video processing that removes components of low tracking interest before compression to minimize bitrate, and uses some of the bitrate savings to introduce redundancy into the transmitted bitstream to reduce the probability of information loss. We show using a common tracker and loss concealment algorithm that our system allows for up to 100 increased tracking accuracy at a given bitrate, or 90 bitrate savings for comparable tracking quality

Low-complexity tracking-aware H.264 video compression for transportation surveillance

In centralized transportation surveillance systems, video is captured and compressed at low processing power remote nodes and transmitted to a central location for processing. Such compression can reduce the accuracy of centrally run automated object tracking algorithms. In typical systems, the majority of communications bandwidth is spent on encoding temporal pixel variations such as acquisition noise or local changes to lighting. We propose a tracking-aware, H.264-compliant compression algorithm that removes temporal components of low tracking interest and optimizes the quantization of frequency coefficients, particularly those that most influence trackers, significantly reducing bitrate while maintaining comparable tracking accuracy. We utilize tracking accuracy as our compression criterion in lieu of mean squared error metrics. Our proposed system is designed with low processing power and memory requirements in mind, and as such can be deployed on remote nodes. Using H.264/AVC video coding and a commonly used state-of-the-art tracker we show that our algorithm allows for over 90 bitrate savings while maintaining comparable tracking accuracy

Tracking-optimal pre- and post-processing for H.264 compression in traffic video surveillance applications

The compression of video can reduce the accuracy of automated tracking algorithms. This is problematic for centralized applications such as transportation surveillance systems, where remotely captured and compressed video is transmitted to a central location for tracking. In typical systems, the majority of communications bandwidth is spent on representing events such as capture noise or local changes to lighting. We propose a pre- and post-processing algorithm that identifies and removes such events of low tracking interest, significantly reducing the bitrate required to transmit remotely captured video while maintaining comparable tracking accuracy. Using the H.264/AVC video coding standard and a commonly used state-of-the-art tracker we show that our algorithm allows for up to 90 bitrate savings while maintaining comparable tracking accuracy

Content-aware H.264 encoding for traffic video tracking applications

The compression of video can reduce the accuracy of tracking algorithms, which is problematic for centralized applications that rely on remotely captured and compressed video for input. We show the effects of high compression on the features commonly used in real-time video object tracking. We propose a computationally efficient Region of Interest (ROI) extraction method, which is used during standard-compliant H.264 encoding to concentrate bitrate on regions in video most likely to contain objects of tracking interest (vehicles). This algorithm is shown to significantly increase tracking accuracy, which is measured by employing a commonly used automatic tracker

Quantization optimized H.264 encoding for traffic video tracking applications

The compression of video can reduce the accuracy of post-compression tracking algorithms. This is problematic for centralized applications such as traffic surveillance systems, where remotely captured and compressed video is transmitted to a central location for tracking. We propose a low complexity optimization framework that automatically identifies video features critical to tracking and concentrates bitrate on these features via quantization tables. Using the H.264 video coding standard and two commonly used state-of-the-art trackers we show that our algorithm allows for over 60 bitrate savings while maintaining comparable tracking accuracy

Application aware approach to compression and transmission of H.264 compressed video for automated and centralized transportation surveillance

Abstract—In this paper we present a transportation video coding and wireless transmission system specifically tailored to automated vehicle tracking applications. By taking into account the video characteristics and the lossy nature of the wireless channels, we propose video preprocessing and error control approaches to enhance tracking performance while conserving bandwidth resources and computational power at the transmitter. Compared with current state-of-the-art H.264-based implementations, our system is shown to yield over 80 % bitrate savings for comparable tracking accuracy. Index Terms—Transportation video, object tracking, surveillance centric coding, preprocessing, forward error correction (FEC), error concealment, H.264/AVC, I

Tracking-optimal error control schemes for H.264 compressed video for vehicle surveillance

In this paper we present a transportation video coding and transmission system specifically tailored to automated vehicle tracking applications. By taking into account the video characteristics and the lossy nature of the wireless channels, we propose error control approaches to enhance tracking accuracy. The proposed system is shown to give performance improvement over the current state-of-the-art system and yields bitrate savings of up to 60

Channel modeling and its effect on the end-to-end distortion in wireless video communications

A major limitation faced by a mobile user is their dependence on a limited battery supply. For wireless video communications, joint source coding and transmission power management (JSCPM) has recently been considered as a means of efficiently allocating transmission energy. In order to reduce complexity, the design of many of these adaptive resource allocation algorithms utilizes simplified channel models that do not account for the burstiness of the channel. We analyze the effects of such channel model simplifications on the end-to-end distortion. We present a channel model that is based on information theoretic considerations, which captures the bursty nature of wireless channels and accounts for packet lengths when calculating the probability of loss. Given the source coding and transmission parameters derived using a simplified channel model, our goal is to analyze how the end-to-end distortion is affected when a more realistic complex channel model is used to simulate losses. Experimental results suggest that the performance gain predictions for JSCPM using a simpler channel model are also valid when more sophisticated channel simulations are used, provided that a number of additional steps are taken after the optimization to account for the complex characteristics of wireless channels. 1