Publications

2022

1. 

   Multiple object tracking in recent times: a literature review

   Mk Bashar, Samia Islam, Kashifa Kawaakib Hussain, Md Bakhtiar Hasan, ABM Rahman, and Md Hasanul Kabir

   arXiv preprint arXiv:2209.04796, 2022

   Abs Bib HTML PDF

   Multiple object tracking gained a lot of interest from researchers in recent years, and it has become one of the trending problems in computer vision, especially with the recent advancement of autonomous driving. MOT is one of the critical vision tasks for different issues like occlusion in crowded scenes, similar appearance, small object detection difficulty, ID switching, etc. To tackle these challenges, as researchers tried to utilize the attention mechanism of transformer, interrelation of tracklets with graph convolutional neural network, appearance similarity of objects in different frames with the siamese network, they also tried simple IOU matching based CNN network, motion prediction with LSTM. To take these scattered techniques under an umbrella, we have studied more than a hundred papers published over the last three years and have tried to extract the techniques that are more focused on by researchers in recent times to solve the problems of MOT. We have enlisted numerous applications, possibilities, and how MOT can be related to real life. Our review has tried to show the different perspectives of techniques that researchers used overtimes and give some future direction for the potential researchers. Moreover, we have included popular benchmark datasets and metrics in this review.

   @article{bashar2022multiple,
     title = {Multiple object tracking in recent times: a literature review},
     author = {Bashar, Mk and Islam, Samia and Hussain, Kashifa Kawaakib and Hasan, Md Bakhtiar and Rahman, ABM and Kabir, Md Hasanul},
     journal = {arXiv preprint arXiv:2209.04796},
     year = {2022},
     doi = {10.48550/arXiv.2209.04796},
     url = {https://arxiv.org/abs/2209.04796},
     dimensions = {true}
   }