Recently, tracking-by-detection has become a popular paradigm in Multiple-object tracking (MOT) for its concise pipeline. Many current works first associate the detections to form track proposals and then score proposalns by manual functions to select the best. However, long-term tracking information is lost in this way due to detection failure or heavy occlusion. In this paper, the Extendable Multiple Nodes Tracking framework (EMNT) is introduced to model the association. Instead of detections, EMNT creates four basic types of nodes including correct, false, dummy and termination to generally model the tracking procedure. Further, we propose a General Recurrent Tracking Unit (RTU++) to score track proposals by capturing long-term information. In addition, we present an efficient generation method of simulated tracking data to overcome the dilemma of limited available data in MOT. The experiments show that our methods achieve state-of-the-art performance on MOT17, MOT20 and HiEve benchmarks. Meanwhile, RTU++ can be flexibly plugged into other trackers such as MHT, and bring significant improvements. The additional experiments on MOTS20 and CTMC-v1 also demonstrate the generalization ability of RTU++ trained by simulated data in various scenarios.

In recent years, the demand for intelligent devices related to the Internet of Things (IoT) is rapidly increasing. In the field of computer vision, many algorithms have been preinstalled in IoT devices to achieve higher efficiency, such as face recognition, area detection, target tracking, etc. Tracking is an important but complex task that needs high efficiency solutions in real applications. There is a common assumption that detection can only represent one pedestrian to describe nonoverlapping in physical space. In fact, the pixels of the image do not exactly correspond to the positions in the real world. In order to overcome the limitation of this assumption, we remove this unreasonable assumption and present a novel idea that each detector response can have multiple labels to describe different targets at the same time. Therefore, we propose a graph-based method for near-online tracking in this article. We introduce a detection multiplexing method for tracking in the monocular image and propose a multiplex labeling graph (MLG) model. Each node in MLG has the ability to represent multiple targets. In addition, we improve the shortage of graph-based trackers in using temporal features. We construct long short-term memory networks to model motion and appearance features for MLG optimization. On the public multiobject tracking challenge benchmark, our near-online method gains satisfactory efficiency and achieves state-of-the-art results without additional private detection as well.

The rapid increase in the volume of video data generated from edges in the Industrial Internet of Things, opens up new possibilities for enhancing the application of video service. Multicamera multiobject tracking (MCMT) has always been a fundamental task in video surveillance or traffic control. However, the traditional MCMT methods are limited by the communication bottleneck and computation resources of the centralized curator, and suffer from security and privacy issues. In this article, we first design multicamera multihypothesis tracking (MC-MHT) framework to achieve real-time tracking performance among edge cameras. The complex association of objects is described by multiskip trees. The tracking task is well distributed to each camera. Then, we integrate multicamera tracking chain into MC-MHT to ensure security and trust. The state transition of targets in multicamera is illustrated from the perspective of blockchain transactions. The transactions are validated by an integrated tracking consensus to counter Byzantine behavior. Numerical results derived from real-world scenarios and CAMPUS dataset show that the proposed method achieves real-time performance (24–36 FPs) and 79.0–82.4 MOTA indicator, as well as reduces identity switch errors about 71% under Byzantine attack.

Multi-object tracking is a basic task in video analysis. Due to the strict requirements on efficiency and resource consumption, most of the applications on edge devices are online or near-online methods. Besides motion modeling, appearance information is also widely used for tracking. However, the influence of occlusion is usually ignored. In this paper, Spatial-Temporal Co-occurrence Constraints (STCC) features are introduced to resist occlusions by exploring the rich spatial and temporal information of tracklets. In addition, a novel blockchain-based near-online framework called Co-occurrence Constraints Tracklet tracker (CoCT) is proposed for cross-camera tracking. It inherits the advantages of blockchain technology in sharing information. Based on blockchain, an efficient association mechanism and a reliable information sharing method are introduced. Experimental results show that CoCT performs high computational efficiency and low resource consumption. In edge computing environment, it achieves real-time performance on cross-camera tracking. On MOT17 benchmark, our method shows state-of-the-art results compared with other online trackers.

For intelligent transportation system, Multiple Object Tracking (MOT) is more challenging from traditional static surveillance camera to mobile devices of Internet of Things (IOT). To cope with this problem, previous works always rely on additional information from multi-vision, various sensors or pre-calibration. Only based on a monocular camera, we propose a hybrid motion model to improve tracking accuracy in mobile devices. First, the model evaluates camera motion hypotheses by measuring optical flow similarity and transition smoothness to perform robust camera trajectory estimation. Second, along the camera trajectory, smooth dynamic projection is used to map objects from image to world coordinate. Third, to deal with trajectory motion inconsistency, which is caused by occlusion and interaction of long time interval, tracklets motion are described by multi-mode motion filter for adaptive modeling. Fourth, in tracklets association, we propose a spatio-temporal evaluation mechanism, which achieves higher discriminability in motion measurement. Experiments on MOT15, MOT17 and KITTI benchmarks show that our proposed method improves the trajectory accuracy especially in mobile devices and our method achieves competitive results over other state-of-the-art methods.

Vehicle Re-Identification (ReID) aims to find images of the same vehicle from different videos. It remains a challenging task in the video analysis field due to the huge appearance discrepancy of the same vehicle in cross-view matching and the subtle difference of different similar vehicles in same-view matching. In this paper, we propose a Co-occurrence Attention Net (CAN) to deal with these two challenges. Specifically, CAN consists of two branches, a main branch and an aware branch. The main branch is in charge of extracting global features that are consistent in most views. This feature encodes holistic information such as color and pose, however, it can not handle cross/same-view hard cases, as shown in Fig.1. Therefore, the aware branch is designed to focus on the local details and viewpoint information, which can become an important complement for those hard cases. Considering that the positions of local areas such as wheels and logos change with the viewpoint, Aware Attention Module is introduced to find the hidden relationship among local areas and seamlessly combine the viewpoint information simultaneously. Then, CAN is trained by a partition-and-reunion-based loss, which can narrow the intra-class distance and increase the inter-class distance. Further, an adaptive co-occurrence view emphasize strategy is adopted to fully utilize the learned features. Experimental results on three widely used datasets including VeRi-776, VehicleID and VERI-Wild demonstrate the effectiveness of our method and competitive performance with other state-of-the-art methods.