Bidirectional Long Short-Term Memory Network for Vehicle Behavior Recognition

Abstract

Vehicle behavior recognition is an attractive research field which is useful for many computer vision and intelligent traffic analysis tasks. This paper presents an all-in-one behavior recognition framework for moving vehicles based on the latest deep learning techniques. Unlike traditional traffic analysis methods which rely on low-resolution videos captured by road cameras, we capture 4K ( 3840 × 2178 ) traffic videos at a busy road intersection of a modern megacity by flying a unmanned aerial vehicle (UAV) during the rush hours. We then manually annotate locations and types of road vehicles. The proposed method consists of the following three steps: (1) vehicle detection and type recognition based on deep neural networks; (2) vehicle tracking by data association and vehicle trajectory modeling; (3) vehicle behavior recognition by nearest neighbor search and by bidirectional long short-term memory network, respectively. This paper also presents experimental results of the proposed framework in comparison with state-of-the-art approaches on the 4K testing traffic video, which demonstrated the effectiveness and superiority of the proposed method.

Keywords

• unmanned aerial vehicles (UAVs)
• deep neural networks
• vehicle detection
• vehicle tracking
• behavior recognition
• long short-term memory