DeepTracking from Aerial Platforms

Sivakumar, Abirami and Shahid, Mohd and S, Muthukumar and Balaji, Anirudh (2021) DeepTracking from Aerial Platforms. In: 2021 Asian Conference on Innovation in Technology, ASIANCON 2021, 28 August 2021 through 29 August 2021, Pune.

Text
2021_Asian_Conference.pdf - Published Version
Restricted to Registered users only
Download (1MB) | Request a copy

Abstract

Object tracking is a pivotal part of video processing. Defense based UAVs generally fly at an altitude of 20,000-30,000ft. Conventionally, it's quite easy to track objects from a stationary camera's feed. Videos from UAVs are hard to process beca1use of abrupt motion of the UAV, jitters, noise and cluttering produced in the video. Tracking and surveillance from such altitudes is quite challenging. This paper illustrates the combination of mod- ified YOLOv4 algorithm with Darknet framework to perform object detection and the DeepSORT tracker to perform object tracking in such challenging environments. The detection model achieves an 11% improvement in Mean Average Precision(mAP) when compared with its predecessors on custom images. When paired with DeepSORT and inferred on a high-end GPU it renders output at 28 FPS, making it suitable for real-time tracking. It is also immune to occlusion and camera movement. The proposed model addresses problems in the existing models that require manual locking of target and high computational complexity. In addition to this, it aims to illustrate automatic tracking of moving objects in real-time. © 2021 IEEE.

[error in script]

IITH Creators: