An improvement of multi-scale covariance descriptor for embedded system

Video surveillance has been a major area of focus for researchers and engineers. Actually, video surveillance includes several useful and complex tasks such as tracking, human detection, re-identification and recognition. Multi-scale covariance (MSCOV) descriptor has recently grown in interest due to its good performances for person detection, re-identification and matching. Unfortunately, its original version requires heavy computations, and it is difficult to be executed in real time on embedded systems. This paper presents two aspects of improvement to adapt the MSCOV descriptor for embedded systems. First, the local binary pattern (LBP) features are introduced and a trade-off between accuracy and processing cost is used to define the best features combination. Second, parallel implementation and embedded co-processor are exploited to accelerate processing time on multi-core CPU architectures. Both optimizations are implemented and evaluated for executing a complete application of person re-identification systems. The software implementation is performed using the VIPeR dataset. Using LBP, 21.57% processing speed-up and 50% less memory requirements for the descriptor computation are achieved without any accuracy performance degradation. We also prototype the proposed design using Zynq platform based on ARM Cortex-A9. The results demonstrate the effectiveness of the parallelization and conduct more than 11 times processing speed-up against the original algorithm.