Real‐time vehicle counting can efficiently improve traffic control and management. Aiming to efficiently collect the real‐time traffic information, the authors propose an effective vehicle counting system for detecting and tracking vehicles in complex traffic scenes. The proposed algorithm detects moving vehicles based on background subtraction method with ‘low‐rank + sparse’ decomposition. For accurately counting vehicles, an online Kalman filter algorithm is used to track the multiple moving objects and avoid counting one vehicle repeatedly. The proposed method is evaluated on three publicly available datasets, which include seven video sequences with various challenging scenes for detection performance evaluation, and another two video sequences for vehicle counting evaluation. The experimental results demonstrate a good performance of the proposed method in terms of both qualitative and quantitative evaluations.

Characterized by the ability to handle varying number of objects, tracking by detection framework becomes increasingly popular in multi-object tracking (MOT) problem. However, the tracking performance heavily depends on the object detector. Considering data association optimization and association affinity model are two key parts in MOT, an online multi-pedestrian tracking method is proposed to formulate a more effective association affinity model. It includes a two-step data association taking advantage of rank based dynamic motion affinity model. The rank based dynamic motion affinity model is used to estimate the object state and refine the trajectory for each of target to achieve the noiseless trajectory. Both these two strategies are beneficial to eliminate ambiguous detection responses during association. To fairly verify the proposed method, three public datasets are adopted. Both qualitative and quantitative experiment results demonstrate the superiorities of the proposed tracking algorithm in comparison with its counterparts.

The gated recurrent unit (GRU) network can effectively capture temporal information for 1D signals, such as electroencephalography and event-related brain potential, and it has been widely used in the field of EEG emotion recognition. However, multi-domain features, including the spatial, frequency, and temporal features of EEG signals, contribute to emotion recognition, while GRUs show some limitations in capturing frequency–spatial features. Thus, we proposed a hybrid architecture of convolutional neural networks and GRUs (CGRU) to effectively capture the complementary temporal features and spatial–frequency features hidden in signal channels. In addition, to investigate the interactions among different brain regions during emotional information processing, we considered the functional connectivity relationship of the brain by introducing a phase-locking value to calculate the phase difference between the EEG channels to gain spatial information based on functional connectivity. Then, in the classification module, we incorporated attention constraints to address the issue of the uneven recognition contribution of EEG signal features. Finally, we conducted experiments on the DEAP and DREAMER databases. The results demonstrated that our model outperforms the other models with remarkable recognition accuracy of 99.51%, 99.60%, and 99.59% (58.67%, 65.74%, and 67.05%) on DEAP and 98.63%, 98.7%, and 98.71% (75.65%, 75.89%, and 71.71%) on DREAMER in a subject-dependent experiment (subject-independent experiment) for arousal, valence, and dominance.

This paper presents a novel multiple objects tracking method by constructing an improved tracklet affinity function to enhance the performance of multiple object tracking (MOT) within a network flow optimization framework. The aims of this paper are to improve the appearance affinity model and motion affinity model of the tracklet affinity function, both models being key factors to limit MOT performance in tracking by detection framework. In the proposed method, the tracklet association is considered as a generalized linear assignment problem relied on tracklets affinity that is calculated by both improved sparse representation appearance model and rank-based motion model. This paper uses the detection confidence to identify the important targets and, hence, to form the templates set. Then, a weighted target templates set is used for sparse representation of the appearance model. The rank-based motion model exploits motion estimation based on spatial information to interpolate the missing objects during tracklets association. Furthermore, we consider the inconsistency of two cues in different situations and set weights for them based on their properties to describe affinity between two tracklets. Our method has been evaluated on three challenging data sets, and the experimental results show that the proposed method has a good tracking performance compared with several state-of-the-art multi-object trackers.