Structural Analysis of Attributes for Vehicle Re-Identification and Retrieval

Vehicle re-identification plays an important role in video surveillance applications. Despite the efforts made on this problem in the past few years, it remains a challenging task due to various factors such as pose variation, illumination changes, and subtle inter-class difference. We believe that the key information for identification has not been well explored in the literature. In this paper, we first collect a vehicle dataset `VAC21' which contains 7129 images of five types of vehicles. Then, we carefully label the 21 classes of structural attributes hierarchically with bounding boxes. To our knowledge, this is the first dataset with several detailed attributes labeled. Based on this dataset, we use the state-of-the-art one-stage detection method, Single-shot Detection, as a baseline model for detecting attributes. Subsequently, we make a few important modifications tailored for this application to improve accuracy: 1) adding more proposals from low-level layers to improve the accuracy of detecting small objects and 2) employing the focal loss to improve the mean average precision. Furthermore, the results of the attribute detection can be applied to a series of vision tasks that focus on analyzing the images of vehicles. Finally, we propose a novel region of interests (ROIs)-based vehicle re-identification and retrieval method in which the ROIs' deep features are used as discriminative identifiers, encoding the structure information of a vehicle. These deep features are input to a boosting model to improve the accuracy. A set of experiments are conducted on the dataset VehicleID and the experimental results show that our method outperforms the state-of-the-art methods.