Efficient background subtraction for real-time tracking in embedded camera networks

Abstract

Background subtraction is often the first step of many computer vision applications. For a background subtraction method to be useful in embedded camera networks, it must be both accurate and computationally efficient because of the resource constraints on embedded platforms. This makes many traditional background subtraction algorithms unsuitable for embedded platforms because they use complex statistical models to handle subtle illumination changes. These models make them accurate but the computational requirement of these complex models is often too high for embedded platforms. In this paper, we propose a new background subtraction method which is both accurate and computational efficient. The key idea is to use compressive sensing to reduce the dimensionality of the data while retaining most of the information. By using multiple datasets, we show that the accuracy of our proposed background subtraction method is comparable to that of the traditional background subtraction methods. Moreover, real implementation on an embedded camera platform shows that our proposed method is at least 5 times faster, and consumes significantly less energy and memory resources than the conventional approaches. Finally, we demonstrated the feasibility of the proposed method by the implementation and evaluation of an end-to-end real-time embedded camera network target tracking application.

Survey on Monitoring Aquatic Debris Using Smartphone-Based Robots

“…Background subtraction method to be used in embedded camera networks [9], it must be precise and computationally feasible. This allows traditional background subtraction algorithms not suitable for embedded platforms because of the illumination changes.…”

Compressive Background Modeling for Foreground Extraction

“…To evaluate the performances of the proposed method, we compare the results of our method with those of five representative background subtraction algorithms: (1) ARCS [12]; (2) CS-MoG [10]; (3) ViBe [8]; (4) KDE [6]; and (5) GMM [2]. We implement the five algorithms by ourselves, and all the parameters in these algorithms use the proposed default values according to the original papers.…”

“…In recent years, there has been a growing interest in compressive sensing (CS) and the idea of CS has also been exploited for background subtraction. In [10], an image is divided into small blocks and random projections based on CS are then computed for each block to reduce the data dimensionality. After this, each projection value is modeled by GMM to determine whether the block belongs to foreground or not.…”

Aquatic debris monitoring using smartphone-based robotic sensors

“…Background subtraction [19] is a widely adopted approach, which, however, often incurs significant computation overhead to resourceconstrained devices. In [26], compressive sensing is applied for background subtraction to reduce computation overhead. In [33], an adaptive background model is proposed to trade off the object detection performance and computation overhead of background subtraction.…”