Online multi-object tracking based on hierarchical association and sparse representation

Lin, Zijian; Zheng, Huicheng; Ke, Bo; Chen, Lvran

doi:10.1109/icip.2017.8296362

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Lin et al introduced a hierarchical association approach consisting of two stages, which relies on sparse representations. The method uses a similarity matrix with fixed weights and a discriminative sparse representation classifier for objects with different and similar appearances, respectively [29].…”

Section: Similarity Calculation and Data Correlation Stagementioning

confidence: 99%

An Improved Algorithm for Grouped Hierarchical Multi-object Tracking Based on Feature and Motion Prediction Enhancement

Zhang,

Cai,

Kao

et al. 2024

Preprint

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Multi-object tracking (MOT) is a significant challenge within the field of computer vision, holding various practical applications. Nevertheless, it is still a challenge to extract more discriminative features, more accurate prediction of target motion trajectories and better matching strategies in tracking tasks. This study delves into a large number of multi-target tracking algorithms and makes improvements in feature extraction, motion prediction, similarity calculation and data association. A dual-attention module has been incorporated into the feature extraction network with the aim of enhancing the feature information; a position and velocity information fusion module is designed in the motion prediction stage to learn the long-term dependency of target motion information; and finally, a pedestrian grouping hierarchical data association module is designed to accomplish tracking. Our study shows that we achieve competitive performance on popular multi-object tracking benchmarks (e.g., MOT16, MOT17) compared to current state-of-the-art methods.

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Section: Similarity Calculation and Data Correlation Stagementioning

confidence: 99%

An Improved Algorithm for Grouped Hierarchical Multi-object Tracking Based on Feature and Motion Prediction Enhancement

Zhang,

Cai,

Kao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Many approaches rely on appearance [17,29,57,59,11,33,47], motion [13], or social cues [20,44]. They are mostly used to associate pairs of detections, and only account for very short-term correlations.…”

Section: Related Workmentioning

confidence: 99%

Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking

Maksai

Fua

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with. Many state-of-the-art approaches now use sequence models to solve this problem but their training can be affected by biases that decrease their efficiency. In this paper, we introduce a new training procedure that confronts the algorithm to its own mistakes while explicitly attempting to minimize the number of switches, which results in better training.We propose an iterative scheme of building a rich training set and using it to learn a scoring function that is an explicit proxy for the target tracking metric. Whether using only simple geometric features or more sophisticated ones that also take appearance into account, our approach outperforms the state-of-the-art on several MOT benchmarks.

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“…As a setback, owing to the poor adaptiveness to the dynamic variations, there are high chances for the occurrence of failure of tracking to the off-line trackers. Therefore, online approaches are utilized for fulfilling the runtime specification necessity of arbitrary tracking that links existent tracks with detecting frame by frame, possessing higher practical utility [14,15]. Recognizing every tracklet (i.e., linked detections in short frames) at frame is considered to be a vital process as the trajectories are constructed based on both the techniques by utilizing global or local associations [16].…”

Section: Introductionmentioning

confidence: 99%

Moving Multi-Object Detection and Tracking Using MRNN and PS-KM Models

Premanand¹,

Kumar²

2023

Computer Systems Science and Engineering

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On grounds of the advent of real-time applications, like autonomous driving, visual surveillance, and sports analysis, there is an augmenting focus of attention towards Multiple-Object Tracking (MOT). The tracking-by-detection paradigm, a commonly utilized approach, connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the similarities of the appearance or the motion between them. For an efficient detection and tracking of the numerous objects in a complex environment, a Pearson Similarity-centred Kuhn-Munkres (PS-KM) algorithm was proposed in the present study. In this light, the input videos were, initially, gathered from the MOT dataset and converted into frames. The background subtraction occurred which filtered the inappropriate data concerning the frames after the frame conversion stage. Then, the extraction of features from the frames was executed. Afterwards, the higher dimensional features were transformed into lower-dimensional features, and feature reduction process was performed with the aid of Information Gaincentred Singular Value Decomposition (IG-SVD). Next, using the Modified Recurrent Neural Network (MRNN) method, classification was executed which identified the categories of the objects additionally. The PS-KM algorithm identified that the recognized objects were tracked. Finally, the experimental outcomes exhibited that numerous targets were precisely tracked by the proposed system with 97% accuracy with a low false positive rate (FPR) of 2.3%. It was also proved that the present techniques viz. RNN, CNN, and KNN, were effective with regard to the existing models.

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Online multi-object tracking based on hierarchical association and sparse representation

Cited by 4 publications

References 16 publications

An Improved Algorithm for Grouped Hierarchical Multi-object Tracking Based on Feature and Motion Prediction Enhancement

An Improved Algorithm for Grouped Hierarchical Multi-object Tracking Based on Feature and Motion Prediction Enhancement

Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking

Moving Multi-Object Detection and Tracking Using MRNN and PS-KM Models

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