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Person Detection Using YOLOv3

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Soft Computing: Theories and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 627))

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Abstract

In today’s world, person detection in video surveillance is very important. It has many applications like crowd counting, single and multiple object tracking, crowd behavior analysis, anomaly detection, etc. There are different models to detect a person in an image and video. But, the majority of the models focused on many object classes which sometimes lead to poor performance for detecting specific objects. In this paper, a single class of object is considered, i.e., person. Here, we have used transfer learning for generating the person detection system by using YOLOv3. We have generated the content specific customized dataset, and annotated the dataset manually by using Label Tool. The result shows that the proposed model detects and classifies the person with higher accuracy.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Banasthali Vidyapith, Banasthali, Rajasthan, 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 Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajesh Kumar

  2. Western Norway University of Applied Sciences, Bergen, Norway

    Ajit Kumar Verma

  3. Department of Computer Science, Shobhit University, Gangoh, India

    Tarun K. Sharma

  4. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Om Prakash Verma

  5. University Institute of Technology, Himachal Pradesh University, Shimla, Himachal Pradesh, India

    Sanjay Sharma

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Tyagi, B., Nigam, S., Singh, R. (2023). Person Detection Using YOLOv3. In: Kumar, R., Verma, A.K., Sharma, T.K., Verma, O.P., Sharma, S. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 627. Springer, Singapore. https://doi.org/10.1007/978-981-19-9858-4_77

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