Video Anomaly Detection in Real Time on a Power-Aware Heterogeneous Platform

Abstract

Field-programmable gate arrays (FPGAs) and graphics processing units (GPUs) are often used when real-time performance in video processing is required. An accelerated processor is chosen based on task-specific priorities (power consumption, processing time, and detection accuracy), and this decision is normally made once at design time. All three of the characteristics are important, particularly in battery-powered systems. Here, we propose a method for moving selection of processing platform from a single design-time choice to a continuous run-time one. We implement Histogram of Oriented Gradients (HOG) for cars and people and Mixture-of-Gaussians motion detectors running across FPGA, GPU, and central processing unit in a heterogeneous system. We use this to detect illegally parked vehicles in urban scenes. Power, time, and accuracy information for each detector is characterized. An anomaly measure is assigned to each detected object based on its trajectory and location compared with learned contextual movement patterns. This drives processor and implementation selection so that scenes with high behavioral anomalies are processed with faster but more power-hungry implementations, but routine or static time periods are processed with power-optimized and less accurate slower versions. Real-time performance is evaluated on video data sets including i-LIDS. Compared with power-optimized static selection, automatic dynamic implementation mapping is 10% more accurate, but draws 12-W of extra power in our testbed desktop system.