Multiple Object Tracking Based on the Deep Neural Networks and Correlation Filter

Zhao, Qingyu; Wang, Lu; Zhou, Zefan

doi:10.1109/robio49542.2019.8961656

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…However, detection-centred tracking methods gained more attention with a considerable progress in OD researches [8]. The data association (DA) process is the key factor of this strategy which is generally considered as '3' separate parts: feature extraction (FE) for candidate representation, affinity metric for assessing the linking probability betwixt candidates or tracklets, and the association algorithm for finding the optimum association [9]. By this means, tracking of failures could be recovered by the technique of determining the object hypothesis similar to the detection techniques [10].…”

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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Section: Introductionmentioning

confidence: 99%