Intelligent Video Surveillance System Using Dynamic Saliency Map and Boosted Gaussian Mixture Model

摘要

In this paper, we propose an intelligent video camera system for traffic surveillance, which can detect moving objects in road, recognize the types of objects, and track their moving trajectories. A dynamic saliency map based object detection model is proposed to robustly detect a moving object against light condition change. A Gaussian mixture model (GMM) integrated with an Adaboosting algorithm is proposed for classifying the detected objects into vehicles, pedestrian and background. The GMM uses C1-like features of HMAX model as input features, which are robust to image translation and scaling. And a local appearance model is also proposed for object tracking. Experimental results plausibly demonstrate the excellence performance of the proposed system.