Combining Background Subtraction Algorithms with Convolutional Neural Network

Accurate and fast extraction of foreground object is a key prerequisite for a wide range of computer vision applications such as object tracking and recognition. Thus, enormous background subtraction methods for foreground object detection have been proposed in recent decades. However, it is still regarded as a tough problem due to a variety of challenges such as illumination variations, camera jitter, dynamic backgrounds, shadows, and so on. Currently, there is no single method that can handle all the challenges in a robust way. In this letter, we try to solve this problem from a new perspective by combining different state-of-the-art background subtraction algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder-decoder fully convolutional neural network architecture is trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different background subtraction algorithms and output a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single background subtraction algorithm. And we show that our solution is more efficient than other combination strategies

Background Subtraction with Real-time Semantic Segmentation

Accurate and fast foreground object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel background subtraction framework with real-time semantic segmentation (RTSS). Our proposed framework consists of two components, a traditional BGS segmenter $\mathcal{B}$ and a real-time semantic segmenter $\mathcal{S}$. The BGS segmenter $\mathcal{B}$ aims to construct background models and segments foreground objects. The real-time semantic segmenter $\mathcal{S}$ is used to refine the foreground segmentation outputs as feedbacks for improving the model updating accuracy. $\mathcal{B}$ and $\mathcal{S}$ work in parallel on two threads. For each input frame $I_t$, the BGS segmenter $\mathcal{B}$ computes a preliminary foreground/background (FG/BG) mask $B_t$. At the same time, the real-time semantic segmenter $\mathcal{S}$ extracts the object-level semantics ${S}_t$. Then, some specific rules are applied on ${B}_t$ and ${S}_t$ to generate the final detection ${D}_t$. Finally, the refined FG/BG mask ${D}_t$ is fed back to update the background model. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves state-of-the-art performance among all unsupervised background subtraction methods while operating at real-time, and even performs better than some deep learning based supervised algorithms. In addition, our proposed framework is very flexible and has the potential for generalization

Investigating the latency cost of statistical learning of a Gaussian mixture simulating on a convolutional density network with adaptive batch size technique for background modeling

Background modeling is a promising field of study in video analysis, with a wide range of applications in video surveillance. Deep neural networks have proliferated in recent years as a result of effective learning-based approaches to motion analysis. However, these strategies only provide a partial description of the observed scenes' insufficient properties since they use a single-valued mapping to estimate the target background's temporal conditional averages. On the other hand, statistical learning in the imagery domain has become one of the most widely used approaches due to its high adaptability to dynamic context transformation, especially Gaussian Mixture Models. Specifically, these probabilistic models aim to adjust latent parameters to gain high expectation of realistically observed data; however, this approach only concentrates on contextual dynamics in short-term analysis. In a prolonged investigation, it is challenging so that statistical methods cannot reserve the generalization of long-term variation of image data. Balancing the trade-off between traditional machine learning models and deep neural networks requires an integrated approach to ensure accuracy in conception while maintaining a high speed of execution. In this research, we present a novel two-stage approach for detecting changes using two convolutional neural networks in this work. The first architecture is based on unsupervised Gaussian mixtures statistical learning, which is used to classify the salient features of scenes. The second one implements a light-weighted pipeline of foreground detection. Our two-stage system has a total of approximately 3.5K parameters but still converges quickly to complex motion patterns. Our experiments on publicly accessible datasets demonstrate that our proposed networks are not only capable of generalizing regions of moving objects with promising results in unseen scenarios, but also competitive in terms of performance quality and effectiveness foreground segmentation. Apart from modeling the data's underlying generator as a non-convex optimization problem, we briefly examine the communication cost associated with the network training by using a distributed scheme of data-parallelism to simulate a stochastic gradient descent algorithm with communication avoidance for parallel machine learnin