Vehicle Reidentification Based on MAPANet and k-Reciprocal Encoding

Vehicle reidentification has important applications in intelligent monitoring systems. However, due to many factors, such as inaccurate vehicle image detection and viewing angle changes, distinguishing features cannot be effectively obtained when the vehicle is reidentified. To improve the recognition ability and robustness of vehicle reidentification, this study proposes a new multiattention part alignment network (MAPANet). The network uses different channels in the feature map to perceive different characteristics of the image clustering of the channels and achieves fine-grained attention to the vehicle. It can automatically locate the distinguishing subregions in the vehicle image and avoid the need for a large number of additional manual pretreatment steps. Moreover, an unsupervised reranking method based on multiple metrics is proposed. The k-reciprocal encoding algorithm can optimize the performance of the sorted list in the reordering problem, recalculate the interclass and intraclass distances of vehicle pictures, and improve sorting results. The experiments in this paper are carried out on the VeRi-776 and VehicleID datasets, and the mean average precision (mAP) results on the two datasets are 72.83% and 75.25%, respectively.

Vehicle Reidentification via Multifeature Hypergraph Fusion

Vehicle reidentification refers to the mission of matching vehicles across nonoverlapping cameras, which is one of the critical problems of the intelligent transportation system. Due to the resemblance of the appearance of the vehicles on road, traditional methods could not perform well on vehicles with high similarity. In this paper, we utilize hypergraph representation to integrate image features and tackle the issue of vehicles re-ID via hypergraph learning algorithms. A feature descriptor can only extract features from a single aspect. To merge multiple feature descriptors, an efficient and appropriate representation is particularly necessary, and a hypergraph is naturally suitable for modeling high-order relationships. In addition, the spatiotemporal correlation of traffic status between cameras is the constraint beyond the image, which can greatly improve the re-ID accuracy of different vehicles with similar appearances. The method proposed in this paper uses hypergraph optimization to learn about the similarity between the query image and images in the library. By using the pair and higher-order relationship between query objects and image library, the similarity measurement method is improved compared to direct matching. The experiments conducted on the image library constructed in this paper demonstrates the effectiveness of using multifeature hypergraph fusion and the spatiotemporal correlation model to address issues in vehicle reidentification.

A Vehicle Reidentification Algorithm Based on Double-Channel Symmetrical CNN

It has become a challenging research topic to accurately identify the vehicles in the past from the mass monitoring data. The challenge is that the vehicle in the image has a large attitude, angle of view, light, and other changes, and these complex changes will seriously affect the vehicle recognition performance. In recent years, the convolutional neural network (CNN) has achieved great success in the field of vehicle reidentification. However, due to the small amount of vehicle annotation in the dataset of vehicle reidentification, the existing CNN model is not fully utilized in the training process, which affects the ability to identify the deep learning model. In order to solve the above problems, a double-channel symmetric CNN vehicle recognition algorithm is proposed by improving the network structure. In this method, two samples are taken as input at the same time, in which each sample has complementary characteristics. In this case, with limited training samples, the combination of inputs will be more diversified, and the training process of the CNN model will be more abundant. Experiments show that the recognition accuracy of the proposed algorithm is better than other existing methods, which further verifies the effectiveness of the proposed algorithm in this study.

Visualizations of Travel Time Performance Based on Vehicle Reidentification Data

This paper provides a visual reference of the breadth of arterial performance phenomena based on travel time measures obtained from reidentification technology that has proliferated in the past 5 years. These graphical performance measures are revealed through overlay charts and statistical distribution as revealed through cumulative frequency diagrams (CFDs). With overlays of vehicle travel times from multiple days, dominant traffic patterns over a 24-h period are reinforced and reveal the traffic behavior induced primarily by the operation of traffic control at signalized intersections. A cumulative distribution function in the statistical literature provides a method for comparing traffic patterns from various time frames or locations in a compact visual format that provides intuitive feedback on arterial performance. The CFD may be accumulated hourly, by peak periods, or by time periods specific to signal timing plans that are in effect. Combined, overlay charts and CFDs provide visual tools with which to assess the quality and consistency of traffic movement for various periods throughout the day efficiently, without sacrificing detail, which is a typical byproduct of numeric-based performance measures. These methods are particularly effective for comparing before-and-after median travel times, as well as changes in interquartile range, to assess travel time reliability.