SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos

With the growing capacity of video devices, human operators are nowadays overwhelmed by the huge volumes of data generated in different applications including surveillance. Therefore, automatic video processing techniques are required in order to filter out uninteresting data and to focus the attention of operators. However, reliability is still a challenging problem. In this paper, we show how spatio-temporal redundancy may be exploited to enhance the accuracy of automatic change detection in aerial videos. More precisely, we present an algorithm based on Belief Propagation in order to improve spatio-temporal consistency between successive change detection results. Experiments demonstrate that our method leads to increased accuracy in change detection.

Change detection in moving-camera videos with limited samples using twin-CNN features and learnable morphological operations

The goal of this special issue was to bring together experts working on applying models, principles, and knowledge of human audio-visual perception and cognition to optimize video processing algorithms and applications. This is an area where our deeper understanding of visual perception to video processing will lead to new breakthroughs in visual information processing. Attentional focus is an important aspect of perception that helps understand user interest and intent. Determining points of saliency is computationally complex. Compressed-domain methods thus become valuable tools for developing computationally efficient and practical solutions. In BCompressed-Domain Correlates of Human Fixations in Dynamic Scenes^ (10. 1007/s11042-015-2802-3) authors present a method for detecting points of fixation in H.264/ AVC video using motion vectors, block coding modes and coded residuals parsed from a H. 164/AVC bitstream. Egocentric videos are captured usingwearable cameras and used to detect a wearer’s point of view. Amount of video captured using wearable cameras is increasing and saliency detection in such videos enables applications such as efficient video summarization. In BGeometrical Cues in Visual Saliency Models for Active Object Recognition in Egocentric Videos^ (10.1007/ s11042-015-2803-2) authors use geometrical cues to improve saliency detection in videos. Action recognition from videos is a challenging task with many applications including surveillance and social behavior understanding. Inspired by models of neural response to visual input, the paper entitled BDeep Learning Human Actions from Video via Sparse Filtering and Locally Competitive Algorithms^ (10.1007/s11042-015-2808-x) presents a method that combines sparse filtering with locally competitive algorithms for applications in action recognition. Multimed Tools Appl (2015) 74:10053–10056 DOI 10.1007/s11042-015-2957-y

One of the fundamental operations in computer vision applications is change detection, in which moving foreground objects are segmented from a static background. A common approach for change detection is the comparison of an image frame with the stored background model using a matching algorithm, a process known as background subtraction. However, such techniques fail in environments with dynamic backgrounds, illumination changes, or shadow and camera jitters. This study focuses on effectively detecting scene changes in complex environments. To this end, we proposed a new colour descriptor named local colour difference pattern (LCDP) that is insusceptible to shadow and is able to capture both colour and texture features at a pixel location. Furthermore, a scene change detection framework was proposed to handle dynamic scenes based on sample consensus that integrates LCDP and a novel spatial model fusion mechanism. Experiments using the CDnet benchmark dataset demonstrated the effectiveness of the proposed approach to change detection in complex environments.

Detecting relevant changes is a fundamental problem of video surveillance. Because of the high variability of data and the difficulty of properly annotating changes, unsupervised methods dominate the field. Arguably one of the most critical issues to make them practical is to reduce their false alarm rate. In this work, we develop a non-semantic, method-agnostic, weakly supervised a-contrario validation process, based on high-dimensional statistical modeling of deep features using a Gaussian mixture model, that can reduce the number of false alarms of any change detection algorithm. We also raise the insufficiency of the conventionally used pixel-wise evaluation, as it fails to precisely capture the performance needs of most real applications. For this reason, we complement pixel-wise metrics with component-wise metrics and evaluate the impact of our approach at both pixel and object levels, on six methods and several sequences from different datasets. Our experimental results reveal that the a-contrario theory can be applied to a statistical model of the background of a scene and largely reduce the number of false positives at both pixel and component levels.

Change detection is required in monitoring and managing complex situations such as air traffic control. Considering that change blindness—the incapacity to detect changes in a visual scene—is a considerable source of human errors and that most studies on change detection have involved static visual scenes, it is crucial to evaluate existing tools designed to help this cognitive function in complex dynamic situations. The goal of the present study was to determine the efficacy of the Change History Explicit (CHEX)—a tool already proven effective when explicit change detection is the only task to execute—when change detection is implicit and intrinsic to a more complex task. Results revealed that the CHEX failed to improve implicit change detection when this task was embedded in a threat-evaluation and weapon-assignment (TEWA) task. Moreover, TEWA performance was hindered and mental workload was perceived as higher when the CHEX was available. Even when the information load imposed by the CHEX was reduced, the tool remained ineffective. This suggests that the nature of the change detection task should be taken into account when designing a decision support system.

The effect of healthy aging on change detection and sensitivity to predictable structure in crowded acoustic scenes

Background subtraction is a widely used technique in motion detection. There are many challenges for motion detection in oceanic video like camera jitter, dynamic background and low visibility. If the background is dynamic or if the background changes overtime, a background update should be done in real time to precisely detect any kind of moving objects. In order to achieve an accurate underwater detection of motion in case of static camera with dynamic background in marine video, a novel detection scheme called sliding windowed fuzzy correlation analysis is proposed. The background modelling is based on sliding window technique, and the detection scheme is based on fuzzy correlation analysis. The window size is fixed to 12 in the algorithm to obtain better results and to reduce the latency in execution. The dataset considered here is ‘dataset on underwater change detection’ (Kaghyan and Sarukhanyan, Int J Inf Model Anal 1:146–156, 2012) that consists of five marine videos along with its ground truth. We qualitatively and quantitatively prove that the proposed method attains better motion detection as compared to other existing methods. The computational complexity involved is Intel Core i5 processor with MATLAB® software for simulation.KeywordsBackground subtractionCamera jitterDynamic backgroundSliding windowUnderwater motion detection

Nowadays, moving object detection in sequence images has become a hot topic in computer vision research, and has a very wide range of practical applications in many fields of military and daily life. In this paper, fast detection of moving objects in complex background is studied, and fast detection methods for moving objects in static and dynamic scenes are proposed respectively. Firstly, based on image preprocessing, aiming at the difficulty of feature extraction of moving targets in low illumination at night, Gamma change is used to process. Secondly, for the fast detection of moving objects in static scenes, this paper designs a detection method combining background difference and edge frame difference. Finally, aiming at the fast detection of moving objects in dynamic scenes, a feature matching detection method based on the SIFT algorithm is designed in this paper. Simulation experiments show that the method designed in this paper has good detection performance.

Change detection performance is influenced by a number of factors, among which is the informativeness of targets. It has not been clarified, yet, whether the highly informative regions have a processing priority as a result of resource deployment from other tasks or whether it results from a better resource management. In this paper, we adopted a change detection paradigm in which thirty participants were randomly assigned to two groups: single (change detection task) and dual task [change detection and a simplified version of the Paced Auditory Serial Oppository Task (PASOT, Gow and Deary in J Clin Exp Neuropsychol 26:723-736, 2004), which implies a verbal effort]. Stimulus informativeness was defined as social relevance, that is, changing targets were people (high relevance) versus objects (low relevance), all other aspects (i.e., salience and position in the scene) kept constant. As hypothesized, data analyses showed a significant main effect of social relevance and task condition, i.e., better change detection performance and lower change detection times for people versus objects and for single than for dual task condition. Interestingly, the PASOT accuracy remained stable across the person versus object trials, thus implying that the better performance with socially relevant targets could not be explained by a resources withdrawal from the secondary task.

In recent years, a digital video stabilization improving the results of hand-held shooting or shooting from mobile platforms is the most popular approach. In this chapter, the task of digital video stabilization in static scenes is investigated. The unwanted motion caused by camera jitters or vibrations ought to be separated from the objects motion in a scene. Our contribution connects with the development of deblurring method to find and improve the blurred frames, which have strong negative influence on the following processing results. The use of fuzzy Takagi-Sugeno-Kang model for detection the best local and global motion vectors is the novelty of our approach. The quality of test videos stabilization was estimated by Peak Signal to Noise Ratio (PSNR) and Interframe Transformation Fidelity (ITF) metrics. Experimental data confirmed that the ITF average estimations increase up on 3–4 dB or 15–20 % relative to the original video sequences.

We consider the problem of novel-view synthesis (NVS) for dynamic scenes. Recent neural approaches have accomplished exceptional NVS results for static 3D scenes, but extensions to 4D time-varying scenes remain non-trivial. Prior efforts often encode dynamics by learning a canonical space plus implicit or explicit deformation fields, which struggle in challenging scenarios like sudden movements or generating high-fidelity renderings. In this paper, we introduce 4D Gaussian Splatting (4DRotorGS), a novel method that represents dynamic scenes with anisotropic 4D XYZT Gaussians, inspired by the success of 3D Gaussian Splatting in static scenes [Kerbl et al. 2023]. We model dynamics at each timestamp by temporally slicing the 4D Gaussians, which naturally compose dynamic 3D Gaussians and can be seamlessly projected into images. As an explicit spatial-temporal representation, 4DRotorGS demonstrates powerful capabilities for modeling complicated dynamics and fine details—especially for scenes with abrupt motions. We further implement our temporal slicing and splatting techniques in a highly optimized CUDA acceleration framework, achieving real-time inference rendering speeds of up to 277 FPS on an RTX 3090 GPU and 583 FPS on an RTX 4090 GPU. Rigorous evaluations on scenes with diverse motions showcase the superior efficiency and effectiveness of 4DRotorGS, which consistently outperforms existing methods both quantitatively and qualitatively.

Traditional unmanned aerial vehicle path planning methods focus on addressing planning issues in static scenes, struggle to balance optimality and real-time performance, and are prone to local optima. In this paper, we propose an improved deep reinforcement learning approach for UAV path planning in dynamic scenarios. Firstly, we establish a task scenario including an obstacle assessment model and model the UAV’s path planning problem using the Markov Decision Process. We translate the MDP model into the framework of reinforcement learning and design the state space, action space, and reward function while incorporating heuristic rules into the action exploration policy. Secondly, we utilize the Q function approximation of an enhanced D3QN with a prioritized experience replay mechanism and design the algorithm’s network structure based on the TensorFlow framework. Through extensive training, we obtain reinforcement learning path planning policies for both static and dynamic scenes and innovatively employ a visualized action field to analyze their planning effectiveness. Simulations demonstrate that the proposed algorithm can accomplish UAV dynamic scene path planning tasks and outperforms classical methods such as A*, RRT, and DQN in terms of planning effectiveness.

Global Navigation Satellite Systems (GNSS), including GPS, BeiDou, GLONASS, Galileo and other systems, are becoming more and more widely used in our today life. As a result, multi-constellation receiver that compatible with more than one system will take the place of single-constellation receiver that only uses GPS or BeiDou system. Reliability monitoring algorithm in single-constellation receiver has some limitations when applied in multi-constellation receiver. Therefore, an enhanced reliability monitoring algorithm based on weighted and statistic schemes is designed for multi-constellation receiver that compatible with GPS and BeiDou in this paper. The experiment results show that the algorithm improves the performance in both static and dynamic scenes, especially about 31 % in static scene than the BeiDou/GPS multi-constellation receiver without it.

This paper presents a robust foreground detection method capable of adapting to different motion speeds in scenes. A key contribution of this paper is the background estimation using a proposed novel algorithm, neighbor-based intensity correction (NIC), that identifies and modifies the motion pixels from the difference of the background and the current frame. Concretely, the first frame is considered as an initial background that is updated with the pixel intensity from each new frame based on the examination of neighborhood pixels. These pixels are formed into windows generated from the background and the current frame to identify whether a pixel belongs to the background or the current frame. The intensity modification procedure is based on the comparison of the standard deviation values calculated from two pixel windows. The robustness of the current background is further measured using pixel steadiness as an additional condition for the updating process. Finally, the foreground is detected by the background subtraction scheme with an optimal threshold calculated by the Otsu method. This method is benchmarked on several well-known data sets in the object detection and tracking domain, such as CAVIAR 2004, AVSS 2007, PETS 2009, PETS 2014, and CDNET 2014. We also compare the accuracy of the proposed method with other state-of-the-art methods via standard quantitative metrics under different parameter configurations. In the experiments, NIC approach outperforms several advanced methods on depressing the detected foreground confusions due to light artifact, illumination change, and camera jitter in dynamic scenes.