FPGA Implementation of a Video Based Abnormal Action Detection System with Real-Time Cubic Higher Order Local Auto-Correlation Analysis
In this paper, we show FPGA implementation of a real-time video-based abnormal action detection system, which is a key basic function of applications such as security systems and monitoring systems for nursing elderly people. Our system extracts Cubic Higher order Local Auto-Correlation (CHLAC) features from input video frames and detects abnormal actions with a subspace method based on Candid Covariance-free Incremental Principal Component Analysis (CCIPCA). Empirical experiments demonstrate our system works well at 62.5 fps, which is limited by a camera device. The system implemented on the FPGA is estimated to achieve up to 240 fps, which corresponds to 8.6 times speedup compare to software execution on a PC. It is also shown that the FPGA implementation is more than 20 times energy efficient than the software execution.
KeywordsSubspace Method FPGA Implementation Software Execution Machine Vision Application Pipeline Schedule
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