Abstract
Wireless Visual Sensor Networks (WVSN) play an essential role in tracking moving objects. WVSN's key drawbacks are storage, power, and bandwidth. Background subtraction is used in the early stages of target tracking to extract moving targets from video images. Many standard methods of subtracting backgrounds are no longer suitable for embedded devices because they use complex statistical models to manage small changes in lighting. This paper introduces a system based on the Partial Discrete Cosine Transform (PDCT), reducing the vast dimensions of processed data while retaining most of the important information, thereby reducing processing and transmission energy. It also uses a dual-mode single Gaussian model (SGM) for accurate detection of moving objects. The proposed system's performance is to be assessed using the standard CDnet 2014 benchmark dataset in terms of detection accuracy and time complexity. Furthermore, the suggested method is compared to previous WVSN background subtraction methods. Simulation results show that the proposed method consistently has 15% better accuracy and is up to 3 times faster than the state-of-the-art object detection methods for WVSN. Finally, we showed the practicality of the suggested method by simulating it in a sensor network environment using the Contiki OS Cooja Simulator and implementing it in a real testbed using Cortex M3 open nodes of IOT-LAB.
Highlights
Wireless sensor networks (WSNs), which are made up of thousands of scalar sensors nodes that are spatially distributed and wirelessly communicated, have attracted researchers' interest [1]
The experimental dataset and setup are explained, the qualitative analysis is shown to illustrate the performance of our system, and evaluation for quantitative and execution performance is done to test the accuracy and running time
When we compared our results to different existing methods published on the CDnet website [39], we identified mixture of Gaussians (MOG) [9], KNN [40], ViBe [12], and SubS [41] as candidates
Summary
Wireless sensor networks (WSNs), which are made up of thousands of scalar sensors nodes that are spatially distributed and wirelessly communicated, have attracted researchers' interest [1]. The WSN's capabilities are being expanded to include sophisticated environmental monitoring, advanced health care delivery, traffic avoidance, fire prevention, and monitoring, as well as object tracking, and modern surveillance systems [2]. WVSN has focused on military, commercial traffic management, and precision agriculture surveillance applications [3]. Sensor nodes' visual processing capability, second, memory storage constraints for sensor nodes and Finlay; communication of large volumes of image data. Maximising network lifespan while processing huge volumes of multimedia data while following application-specific QoS requirements such as latency, packet loss, bandwidth, and throughput is a challenge. In addition to developing energy-sensitive multimedia processing algorithms and infrastructures, it is necessary to establish efficient communication strategies [3]
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