Multi-Object Tracking on SWIR Images for City Surveillance in an Edge-Computing Environment

Although Short-Wave Infrared (SWIR) sensors have advantages in terms of robustness in bad weather and low-light conditions, the SWIR images have not been well studied for automated object detection and tracking systems. The majority of previous multi-object tracking studies have focused on pedestria...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-07, Vol.23 (14), p.6373
Main Authors: Park, Jihun, Hong, Jinseok, Shim, Wooil, Jung, Dae-Jin
Format: Article
Language:English
Subjects:
Algorithms
Automation
Datasets
deep learning
edge computing
multi-object tracking
neural network quantization
Neural networks
object detection
Privacy
Privacy, Right of
Sensors
short-wave infrared
Surveillance
Telematics
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Summary:Although Short-Wave Infrared (SWIR) sensors have advantages in terms of robustness in bad weather and low-light conditions, the SWIR images have not been well studied for automated object detection and tracking systems. The majority of previous multi-object tracking studies have focused on pedestrian tracking in visible-spectrum images, but tracking different types of vehicles is also important in city-surveillance scenarios. In addition, the previous studies were based on high-computing-power environments such as GPU workstations or servers, but edge computing should be considered to reduce network bandwidth usage and privacy concerns in city-surveillance scenarios. In this paper, we propose a fast and effective multi-object tracking method, called Multi-Class Distance-based Tracking (MCDTrack), on SWIR images of city-surveillance scenarios in a low-power and low-computation edge-computing environment. Eight-bit integer quantized object detection models are used, and simple distance and IoU-based similarity scores are employed to realize effective multi-object tracking in an edge-computing environment. Our MCDTrack is not only superior to previous multi-object tracking methods but also shows high tracking accuracy of 77.5% MOTA and 80.2% IDF1 although the object detection and tracking are performed on the edge-computing device. Our study results indicate that a robust city-surveillance solution can be developed based on the edge-computing environment and low-frame-rate SWIR images.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23146373