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.

Recently, most multiple object tracking (MOT) algorithms adopt the idea of tracking-by-detection. Relevant research shows that the performance of the detector obviously affects the tracker, while the improvement of detector is gradually slowing down in recent years. Therefore, trackers using tracklet (short trajectory) are proposed to generate more complete trajectories. Although there are various tracklet generation algorithms, the fragmentation problem still often occurs in crowded scenes. In this paper, we introduce an iterative clustering method that generates more tracklets while maintaining high confidence. Our method shows robust performance on avoiding internal identity switch. Then we propose a deep association method for tracklet association. In terms of motion and appearance, we construct motion evaluation network (MEN) and appearance evaluation network (AEN) to learn long-term features of tracklets for association. In order to explore more robust features of tracklets, a tracklet-based training mechanism is also introduced. Tracklet groups are used as the input of the networks instead of discrete detections. Experimental results show that our training method enhances the performance of the networks. In addition, our tracking framework generates more complete trajectories while maintaining the unique identity of each target as the same time. On the latest MOT 2017 benchmark, we achieve state-of-the-art results.

Tracking-by-detection is one of the most popular approaches for tracking multiple objects in which the detector plays an important role. Sometimes detector failures caused by occlusions or various poses are unavoidable and lead to tracking failure. To cope with this problem, we construct a heterogeneous association graph that fuses high-level detections and low-level image evidence for target association. Compared with other methods of using low-level information, our proposed Heterogeneous Association Fusion(HAF) tracker is less sensitive to particular parameters and is easier to extend and implement. We use the fused association graph to build track trees for HAF and solve them by the multiple hypotheses tracking (MHT) framework, which has been proven competitive by introducing efficient pruning strategies. In addition, a novel idea of adaptive weights is proposed to analyze the contribution between motion and appearance. We also evaluate our results on the MOT challenge benchmarks and achieve state-of-the-art results on the MOT Challenge 2017.

This paper proposes a novel iterative maximum weighted independent set(MWIS) algorithm for multiple hypothesis tracking(MHT) in a tracking-by-detection framework. MHT converts the tracking problem into a series of MWIS problems across the tracking time. Previous works solve these NP-hard MWIS problems independently without the use of any prior information from each frame, and they ignore the relevance between adjacent frames. In this work, we iteratively solve the MWIS problems by using the MWIS solution from the previous frame rather than solving the problem from scratch each time. First, we define five hypothesis categories and hypothesis transfer model, which explicitly describes the hypothesis relationship between adjacent frames. We also propose a polynomial-time approximation algorithm for the MWIS problem in MHT. In addition to that, we present a confident short tracklet generation method and incorporate tracklet-level association into MHT, which further improve the computational efficiency. Our experiments on both MOT16 and MOT17 benchmarks show that our tracker outperforms all the previously published tracking algorithms on both MOT16 and MOT17 benchmarks. Lastly, we demonstrate that the polynomial-time approximate tracker reaches nearly the same tracking performance.

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.