Support vector machine FPGA implementation for video shot boundary detection application

This paper presents a video shot boundary detection system based on support vector machine (SVM) classification method. A hardware fully-parallel digital support vector machine (SVM) classifier is used to detect the shot boundary in a continuous video stream. The throughput is increased by employing a pipelined architecture in the feature extraction stage. Hardware SVM can detect both cut and gradual transition in the video stream. Random pseudo-sampling techniques are employed to solve the class imbalance problem in SVM training. The internal wordlength is optimized for performance and hardware complexity. The threshold method in the postprocessing stage merges small subshots to reduce false alarms. The complete system is demonstrated on Xilinx Virtex IV XC4VSX35 FPGA platform to achieve 256 frames per second.