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Human Detection and Tracking Based on YOLOv3 and DeepSORT

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Communication and Intelligent Systems (ICCIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 686))

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Abstract

This paper proposes an enhanced object tracking architecture based on YOLOv3 and DeepSORT. YOLOv3 is used for object detection, but in this case, we have only selected the human class. For object tracking, the DeepSORT, Kalman filter, and Hungarian algorithm are used. The occlusion issue is solved using the Kalman filter. The parallel approach is used in the suggested architecture to increase the base method's speed. In a parallel approach, the input frame is fed simultaneously to the object detector and the object tracker, enabling the tracker to begin following a person as soon as the object detector recognizes them. Here, occlusion and speed are the two issues that we are concentrating on. The experimental outcomes using specific data demonstrate positive outcomes with faster operation.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Banasthali Vidyapith, Banasthali, Rajasthan, 304022, India

    Bhawana Tyagi, Swati Nigam & Rajiv Singh

Authors
  1. Bhawana Tyagi
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  2. Swati Nigam
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  3. Rajiv Singh
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Corresponding author

Correspondence to Rajiv Singh .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India

    Harish Sharma

  2. National Institute of Technology Delhi, New Delhi, Delhi, India

    Vivek Shrivastava

  3. Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Madhya Pradesh, India

    Kusum Kumari Bharti

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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Tyagi, B., Nigam, S., Singh, R. (2023). Human Detection and Tracking Based on YOLOv3 and DeepSORT. In: Sharma, H., Shrivastava, V., Bharti, K.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2022. Lecture Notes in Networks and Systems, vol 686. Springer, Singapore. https://doi.org/10.1007/978-981-99-2100-3_11

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