Background subtraction is a basic task in computer vision and video processing often applied as a pre-processing step for object tracking, people recognition, etc. Recently, a number of successful background subtraction algorithms have been proposed, however nearly all of the top-performing ones are supervised. Crucially, their success relies upon the availability of some annotated frames of the test video during training. Consequently, their performance on completely unseen videos is undocumented in the literature. In this work, we propose a new, supervised, background-subtraction algorithm for unseen videos (BSUV-Net) based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame. On the CDNet-2014 dataset, BSUV-Net outperforms state-of-the-art algorithms evaluated on unseen videos in terms of F-measure, recall and precision metrics.
@cite_16: Given the existence of many change detection algorithms, each with its own peculiarities and strengths, we propose a combination strategy, that we termed IUTIS (In Unity There Is Strength), based on a genetic Programming framework. This combination strategy is aimed at leveraging the strengths of the algorithms and compensate for their weakness. In this paper we show our findings in applying the proposed strategy in two different scenarios. The first scenario is purely performance-based. The second scenario performance and efficiency must be balanced. Results demonstrate that starting from simple algorithms we can achieve comparable results with respect to more complex state-of-the-art change detection algorithms, while keeping the computational complexity affordable for real-time applications.
@cite_22: Accurate and fast extraction of foreground objects is a key prerequisite for a wide range of computer vision applications, such as object tracking and recognition. Thus, many background subtraction (BGS) methods for foreground object detection have been proposed in recent decades. However, this is still regarded as a tough problem due to a variety of challenges, such as illumination variations, camera jitter, dynamic backgrounds, and shadows. Currently, there is no single method that can handle all the challenges in a robust way. We try to solve this problem from a perspective of combining different state-of-the-art BGS algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder–decoder fully convolutional neural network architecture is adapted and trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different BGS algorithms and produce a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single BGS algorithms. We show that our solution is more efficient than other BGS combination strategies.
As we have pointed out, many different algorithms have been designed to solve the BGS problem, and they all have some advantages and disadvantages. Bianco al introduced an algorithm called IUTIS which combines the results produced by several BGS algorithms @cite_16 . They used genetic programming to to determine how to combine several BGS algorithms using a sequence of basic binary operations, such as logical and or, majority voting and median filtering. Their best result was achieved by using 5 top-performing BGS algorithms on the CDNet-2014 dataset at the time of publication. Zeng al followed the same idea, but instead of genetic programming used a fully-convolutional neural network to fuse several BGS results into a single output @cite_22 , and outperformed IUTIS on CDNet-2014.