One of important Internet of Things applications is to use image and video to realize automatic people monitoring, surveillance, tracking, and re-identification. Despite some recent advances, pedestrian re-identification (Re-ID) remains a challenging task. Existing algorithms based on fully-supervised learning for it usually requires numerous labeled image and video data, while often ignoring the problem of data imbalance. This work proposes a method based on unlabeled samples generated by Cycle Generative Adversarial Networks. For a newly generated unlabeled sample, it learns its pseudo relationship between unlabeled samples and labeled ones in a low dimensional space by using a self-paced learning approach. Then, these unlabeled ones having pseudo relationship with labeled ones are added in a training set to better mine discriminative information between positive and negative samples, which is in turn used to learn a more effective metric. We name this method as a semi-supervised learning approach based on the built pseudo pairwise relations between labeled data and unlabeled one. It can greatly enhance the performance of pedestrian Re-ID in case of insufficient labeled images. By using only about 10% labeled images in Market-1501 and Duke MTMC-Re-ID, the proposed method obtains higher accuracy than state-of-the-art supervised learning methods using all labeled ones. In terms of rank-1 accuracy, it significantly outperforms such methods as deep-learning ones, thus greatly advancing the field of pedestrian Re-ID.

Pedestrian re-identification (Re-ID) is a very challenging and unavoidable problem in the field of multi-camera surveillance in smart transportation. Among many ways to solve this problem, keep it simple and straightforward (KISS) metric learning (KISSME) stands out since it has unbeatable advantages in running time while maintaining highly acceptable matching rate. It can be used to realize effective pedestrian Re-ID in an open world. Although it has achieved highly acceptable performance in some applications, it encounters a small sample size (S)³ problem that causes too small eigenvalues of its covariance matrix, thus resulting in an instability issue. Its large eigenvalues are overestimated; while its small ones are underestimated. In order to solve this problem, we use an orthogonal basis vector to generate virtual samples to overcome the S³ problem. The resulting algorithm named KISS+ is experimentally shown to have the eigenvalues of its covariance matrix significantly larger than those of the original KISSME. In order to show its advantage in pedestrian Re-ID, this work uses multi-feature fusion to extract more discriminant features, and obtain a low-dimensional expression of features through dimension reduction. Experiments based on several well-known databases show that our method can improve the matching rate, while maintaining the advantage of fast computation. Compared with deep learning algorithms, our algorithm does not achieve their matching rate, but it is highly suitable for real-time pedestrian Re-ID of an open world due to its simplicity, easy operation and fast execution.

This work investigates whether virtual samples can solve the small sample size (S³) problem of Keep-It-Simple-and- Straightforward Metric Learning (KISSME) in Pedestrian re-identification (Re-ID). Re-ID is a very challenging and important problem in the field of multi-camera surveillance in smart transportation. Among many hand-crafted ways (not deeply-learned ones) to solve it, KISSME has received great attention. Although it has achieved convincing performance in some applications, it encounters an S³ problem in calculating various classes of covariance matrices whose eigenvalues become too small. Such small eigenvalues cause an instability issue when computing the inverse of covariance matrices, thus resulting in poor Re-ID performance. If we can increase the number of samples, then an S³ problem is alleviated or eliminated. This work makes a hypothesis that virtual samples can do so, and proposes a new algorithm to generate them. It adopts a Genetic Algorithm to generate virtual features (corresponding to virtual samples) based on the dimension-reduced sample features, which eliminates the process of re-extracting features of newly generated virtual samples and save time. It can clearly increase the magnitude of otherwise small eigenvalues, helps one perform the accurate estimation of the inverse of various covariance matrices and finally alleviates the S³ problem. Experimental results based on a commonly-used database confirm that the proposed method can significantly improve the matching rate of pedestrian Re-ID, which fully shows that virtual samples are indeed effective for alleviating the S³ problem in pedestrian Re-ID.

In multirobot patrolling problems, various dynamic situations require a higher level of cooperation among robots. The dynamic problems caused by moving objects are rarely studied yet. This work proposes a distributed event-driven cooperative strategy for multirobot systems to patrol moving objects autonomously. First, forward and backward utility functions are defined as criteria for robots to conduct two-way evaluation when they choose their targets to patrol. Then, three event types and a cooperative action considering energy consumption and visiting frequency comprehensively are proposed to improve coordination among robots during their execution processes. In simulation experiments, the proposed strategy shows significant advantages on decreasing the average and maximum unvisited time of moving objects compared with the state-of-the-art. A marine pollution monitoring case is simulated to demonstrate the practicability of this strategy.

Generic person tracking is a basic task in visual surveillance by using camera as sensors. Many deep learning-based trackers have obtained outstanding performance. Among them, trackers based on Siamese networks have drawn great attention and are promising. These training methods are competitive but training data can be more effectively augmented to improve their person tracking performance. Many other trackers use only one layer to extract semantic features, likely hindering their discriminative learning. In this paper, we propose an enhanced discriminative model prediction method with efficient data augmentation and robust feature fusion. Specifically, we propose to implement an effective data augmentation strategy (e.g., color jitter and motion blur) to unleash the greater potential of original training data. We also adopt a multi-layer feature fusion to obtain a more discriminative feature map. Thus, the proposed tracker can discriminate an object in complicated scenarios in real time. We conduct extensive experiments on two datasets, i.e., VOT2018 and UAV123. Objective evaluation on VOT2018 demonstrates that with its expected average overlap value of 0.430, it outperforms a state-of-the-art tracker by 4.88%. On UAV123, it does so by 4.5% in success rate and 4.4% in precision rate. In addition, our further experimental results reveal that our algorithm can reach a speed that is high enough to meet the real-time tracking requirement when camera are used as sensors.