SmartSORT: an MLP-based method for tracking multiple objects in real-time

Meneses, Michel C.; Matos, Leonardo Nogueira; Prado, Bruno; Carvalho, André C. P. L. F. de; Macedo, Hendrik

doi:10.1007/s11554-020-01054-y

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Fast R-CNN [84] and its variants, such as Mask R-CNN [100] and Faster R-CNN [101] are other prominent object detection methods used within the context of object tracking for the detection stage. Meneses et al [79] used R-CNN [99] to extract the detection features. Garcia and Younes [75] used Faster R-CNN [101] for object detection, where they trained the network on 8746 images of a mock drogue for its application to detecting a beacon.…”

Section: Amentioning

confidence: 99%

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Object Tracking Using Computer Vision: A Review

Kadam,

Fang,

Zou

2024

Computers

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Object tracking is one of the most important problems in computer vision applications such as robotics, autonomous driving, and pedestrian movement. There has been a significant development in camera hardware where researchers are experimenting with the fusion of different sensors and developing image processing algorithms to track objects. Image processing and deep learning methods have significantly progressed in the last few decades. Different data association methods accompanied by image processing and deep learning are becoming crucial in object tracking tasks. The data requirement for deep learning methods has led to different public datasets that allow researchers to benchmark their methods. While there has been an improvement in object tracking methods, technology, and the availability of annotated object tracking datasets, there is still scope for improvement. This review contributes by systemically identifying different sensor equipment, datasets, methods, and applications, providing a taxonomy about the literature and the strengths and limitations of different approaches, thereby providing guidelines for selecting equipment, methods, and applications. Research questions and future scope to address the unresolved issues in the object tracking field are also presented with research direction guidelines.

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