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Video preserves valuable raw information. Understanding these data and then  recognizing objects and tagging them are crucial to intelligent planning and  decision making. Deep learning provides us an effective way to understand  big data with a human-level. As traffic video is characterized by crowded  scene and low definition, it will be non-effective to deal with the whole  image once. An alternative way is to separate image and determine a small  window for each moving object. A Q-learning based moving object recognition  approach, which firstly finds out moving object region and then uses a  Q-learning based optimization method to determine the most compact region  that contain the moving object, is proposed. The algorithms enable to detect  the most compact rectangle around the moving object at near real-time speed.  After that, a deep neural network is used to semantic tag the recognized  objects. The experiment results show the algorithms work effectively.

Human-level moving object recognition from traffic video