Spatiotemporal Activity Mapping for Enhanced Multi-Object Detection with Reduced Resource Utilization
-
Published:2022-12-22
Issue:1
Volume:12
Page:37
-
ISSN:2079-9292
-
Container-title:Electronics
-
language:en
-
Short-container-title:Electronics
Author:
Shashank ,Sreedevi Indu
Abstract
The accuracy of data captured by sensors highly impacts the performance of a computer vision system. To derive highly accurate data, the computer vision system must be capable of identifying critical objects and activities in the field of sensors and reconfiguring the configuration space of the sensors in real time. The majority of modern reconfiguration systems rely on complex computations and thus consume lots of resources. This may not be a problem for systems with a continuous power supply, but it can be a major set-back for computer vision systems employing sensors with limited resources. Further, to develop an appropriate understanding of the scene, the computer vision system must correlate past and present events of the scene captured in the sensor’s field of view (FOV). To address the abovementioned problems, this article provides a simple yet efficient framework for a sensor’s reconfiguration. The framework performs a spatiotemporal evaluation of the scene to generate adaptive activity maps, based on which the sensors are reconfigured. The activity maps contain normalized values assigned to each pixel in the sensor’s FOV, called normalized pixel sensitivity, which represents the impact of activities or events on each pixel in the sensor’s FOV. The temporal relationship between the past and present events is developed by utilizing standard half-width Gaussian distribution. The framework further proposes a federated optical-flow-based filter to determine critical activities in the FOV. Based on the activity maps, the sensors are re-configured to align the center of the sensors to the most sensitive area (i.e., region of importance) of the field. The proposed framework is tested on multiple surveillance and sports datasets and outperforms the contemporary reconfiguration systems in terms of multi-object tracking accuracy (MOTA).
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference30 articles.
1. (2022, August 22). AI in Computer Vision Market Research Report by Component (Hardware, Software), Vertical (Healthcare, Security, Automotive, Agriculture, Sports & Entertainment, and Others), and Region–Global Forecast to 2027. Available online: https://www.expertmarketresearch.com/reports/ai-in-computer-vision-market. 2. Tadic, V., Toth, A., Vizvari, Z., Klincsik, M., Sari, Z., Sarcevic, P., Sarosi, J., and Biro, I. (2022). Perspectives of RealSense and ZED Depth Sensors for Robotic Vision Applications. Machines, 10. 3. and Sreedevi, I. (2022). Distributed Network of Adaptive and Self-Reconfigurable Active Vision Systems. Symmetry, 14. 4. Li, S., Huang, M., Guo, M., and Yu, M. (2021). Evaluation model of autonomous vehicles’ speed suitability based on overtaking frequency. Sensors, 21. 5. AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimation;Saini;IEEE Robot. Autom.,2022
|
|