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. 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. 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. 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.