PhD, University of Cincinnati, 2019, Engineering and Applied Science: Computer Science and Engineering

Wireless camera networks are ubiquitously deployed in various distributed sensing applications. The basic functions of each sensor node include video capture, video encoding or local video processing, and data transmission. The process of video analysis is implemented either in the central server or in the sensor node. Automatic video analysis can efficiently extract useful information from a huge amount of videos without human intervention. Object detection is the first and the most essential step of automatic video analysis. Thanks to abundant information provided by cameras and the development of computer vision techniques, automatic video analysis in wireless distributed systems is applied further. However, traditional network quality measures, such as QoS and QoE, do not necessarily reflect the quality of automatic video analysis in wireless camera networks. The overall goal of this dissertation is to propose new quality measures that could reflect the quality of automatic video analysis in wireless camera networks and to design efficient video processing and encoding schemes for wireless cameras that could boost the quality of automatic video analysis. The impact of lossy compression on object detection is systematically investigated. It has been found that current standardized video encoding schemes cause temporal domain fluctuation for encoded blocks in stable background areas and spatial texture degradation for encoded blocks in dynamic foreground areas of a raw video, both of which degrade the accuracy of object detection. Two measures, the sum-of-absolute frame difference (SFD) and the degradation of texture (TXD), are introduced to depict the temporal domain fluctuation and the spatial texture degradation in an encoded video, respectively. A model of object detection quality on compressed videos is established based on these two measures. Then we have proposed an efficient video encoding framework for boosting the accuracy of object detection for dist (open full item for complete abstract)

Committee: Rui Dai Ph.D. (Committee Chair); Dharma Agrawal D.Sc. (Committee Member); H. Howard Fan Ph.D. (Committee Member); Carla Purdy Ph.D. (Committee Member); Julian Wang Ph.D. (Committee Member) Subjects: Computer Science