Unified method for vehicle detection and attribute recognition

作者：Guo Shaobo; Liu Xu; Wang Zilei

来源：中国图象图形学报, 2017, 22(11).

DOI：10.11834/jig.170146

• vehicle detection
• vehicle attributes recognition
• hard example mining
• long-tail distribution
• multitask learning

Objective Vehicle detection and attribute recognition are the basic tasks in an intelligent traffic system (ITS),which aims to extract the key features of target vehicles.Most solutions separate the key features into several individual modules,such as vehicle detection,vehicle color recognition,and vehicle type recognition.However,such type of solution suffers from many problems under the practical scenario.First,the coupling problem between detection and recognition algorithms increases the complexity of algorithm designation.Second,deep learning-based algorithms are data-driven methods;thus,the algorithm designer should collect data for every single function module for training.However,data collection is costly and time consuming.Moreover,the more the ITS modules ITS,the higher the cost of the computational and communication resources.We propose a unified framework,which is integrated with the vehicle detection and attribute recognition functions,to settle these issues.Method Vehicle detection and attribute recognition tasks can be viewed as a classification problem between background and foreground regions.Color and type are two important holistic features of a vehicle.Combining the two features as the foreground region label can enlarge the diversity between foreground and background regions.The more the diversity between foreground and background regions,the lesser the false positive and true negative detection cases.We utilize the scalability of the multitask learning algorithm to finish vehicle attribute recognition and detection tasks at the same time to implement this idea.Specifically,the multitask paradigm is added on top of the region-based detection algorithm.At the training phase,instead of deploying the raw multitask learning algorithm,we integrate the online hard example mining algorithm into our framework to cope with the negative effect caused by the long-tail phenomenon.At the prediction phase,the proposed framework outputs the vehicle location,vehicle color,and vehicle type information in forward pass.Result We construct a large-scale on-road vehicle dataset,which contains 12 712 images and 19 398 vehicles,in verifying the proposed vehicle detection and attribute recognition framework.In this image dataset,every vehicle in the image is annotated with a bounding box and its corresponding type and color label.We achieve a mean average precision of 85.6％,which is better than that of the SSD and Faster-RCNN algorithms.For the recognition tasks,we achieve 91.3％ and 91.8％ accuracy for color and type recognition,respectively.Conclusion Type and color are two important vision cues for vehicles.Thus,integrating these attributes into the detection algorithm can boost the detection performance to another level and result in a good recognition performance.Moreover,a highly integrated system can make the ITS computationally efficient.

• 出版日期2017
• 单位中国科学技术大学