A Method for Real-time Object Detection in the Surveillance System

Abstract

Since surveillance cameras are installed in high, the objects in the taken images are relatively small. Detecting small objects is a hard issue for the one-stage detector, and its performance in the surveillance system is not good. Two-stage detectors work better, but their speed is too slow to use in the real-time system. To remedy the drawbacks, we propose an efficient method FSSSD which is an extension of famous SSD. The proposed method has three key points: high-resolution inputs to detect small objects, a lightweight Backbone to speed up, and prediction blocks to enrich features. FSSSD achieve 63.7\% AP at 16.7 FPS in the UA-DETRAC test dataset. The performance is similar to two-stage detectors and faster than any other one-stage method.