International Journal of Advance Computational Engineering and Networking (IJACEN)
Follow Us On :
current issues
Volume-12,Issue-4  ( Apr, 2024 )
Past issues
  1. Volume-12,Issue-3  ( Mar, 2024 )
  2. Volume-12,Issue-2  ( Feb, 2024 )
  3. Volume-12,Issue-1  ( Jan, 2024 )
  4. Volume-11,Issue-12  ( Dec, 2023 )
  5. Volume-11,Issue-11  ( Nov, 2023 )
  6. Volume-11,Issue-10  ( Oct, 2023 )
  7. Volume-11,Issue-9  ( Sep, 2023 )
  8. Volume-11,Issue-8  ( Aug, 2023 )
  9. Volume-11,Issue-7  ( Jul, 2023 )
  10. Volume-11,Issue-6  ( Jun, 2023 )

Statistics report
Jul. 2024
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 136
Paper Published : 1596
No. of Authors : 4179
  Journal Paper

Paper Title :
Real-Time Cyclist Tracking in a Video using CNN and Deep Sort

Author :Praveenkumar S M, Prakashgouda Patil, P.S. Hiremath

Article Citation :Praveenkumar S M ,Prakashgouda Patil ,P.S. Hiremath , (2022 ) " Real-Time Cyclist Tracking in a Video using CNN and Deep Sort " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 1-6, Volume-10,Issue-2

Abstract : Abstract - Due to ever increasing environmental safety concerns, cycles and electric vehicles promise to be the most popular modes of transport. Further, the advancements in autonomous electric vehicles pose a greater threat for pedestrians and cyclists, in terms of road safety, who would be vulnerable as road users. Thus, cyclist detection and tracking in videos is a new dimension in multi-object tracking problems encountered in computer vision research. Calculating speed of cyclist is also important for autonomous vehicles to avoid collision between cyclist and vehicle. In this paper, a novel approach for detection, tracking and calculating the speed of cyclist in videos is proposed. The cyclist detection is done by modified YOLOv3 and real-time cyclist tracking is done by employing Deep SORT. The movement representation and data association algorithms are used for cyclist tracking and the optical flow is used to calculate the speed of cyclist. The proposed algorithm is experimented on two benchmark datasets, namely, KITTI and SCD datasets, of videos of real scenes with cyclists. Keywords - Convolutional Neural Network, Video Processing, Multi Object Tracking, Data Association

Type : Research paper

Published : Volume-10,Issue-2


Copyright: © Institute of Research and Journals

| PDF |
Viewed - 54
| Published on 2022-04-28
IRAJ Other Journals
IJACEN updates
Paper Submission is open now for upcoming Issue.
The Conference World