  Intelligent resident surveillance is one of the most essential smart
community services. The increasing demand for security needs surveillance
systems to be able to detect anomalies in surveillance scenes. Employing
high-capacity computational devices for intelligent surveillance in residential
societies is costly and not feasible. Therefore, we propose anomaly detection
for intelligent surveillance using CPU-only edge devices. A modular framework
to capture object-level inferences and tracking is developed. To cope with
partial occlusions, posture deformations, and complex scenes, we employed
feature encoding and trajectory association governed by two metrices
complementing to each other. The elements of an anomaly detection framework are
optimized to run on CPU-only edge devices with sufficient frames per second
(FPS). The experimental results indicate the proposed method is feasible and
achieves satisfactory results in real-life scenarios.

Computer Vision and Pattern Recognition;Artificial Intelligence