Executing video analytics tasks using a large camera network is a challenging problem in the field of video processing. Video compression is a necessary step to reduce video data size before transmission. However, the performance of video analytics tasks generally degrade as video quality drops. This paper considers how to find the optimal point between video compression and performance for the video analytics task of activity recognition. We propose a system that predicts the success or failure of a video analytics task under different compression parameters without executing the task. The system is designed to automatically select the best compression rate for each video to maintain an acceptable detection accuracy. Our experiments indicate that such a system has the potential to improve overall performance across a variety of different activity sets selected from the UCF101 dataset [1].
Time domain continuous imaging (TDCI) centers on the capture and representation of time-varying image data not as a series of frames, but as a compressed continuous waveform per pixel. A high-dynamic-range (HDR) image can be computationally synthesized from TDCI data to represent any virtual exposure interval covered by the waveforms, thus allowing both exposure start time and shutter speed to be selected arbitrarily after capture. This also enables extraction of video with arbitrary framerate and shutter angle. This paper presents the design, and discusses performance, of the first complete, fully open source, infrastructure supporting experimental use of TDCI: TIK (Temporal Imaging from Kentucky or Temporal Image Kontainer). The system not only provides for processing TDCI .tik files, but also allows conventional video files and still image sequences to be converted into TDCI .tik files.