Object Detection In Video Surveillance Research Articles

Anchor boxes adaptive optimization algorithm for maritime object detection in video surveillance

With the development of the marine economy, video surveillance has become an important technical guarantee in the fields of marine engineering, marine public safety, marine supervision, and maritime traffic safety. In video surveillance, maritime object detection (MOD) is one of the most important core technologies. Affected by the size of maritime objects, distance, day and night weather, and changes in sea conditions, MOD faces challenges such as false detection, missed detection, slow detection speed, and low accuracy. However, the existing object detection algorithms usually adopt predefined anchor boxes to search and locate for objects of interest, making it difficult to adapt to maritime objects’ complex features, including the varying scale and large aspect ratio difference. Therefore, this paper proposes a maritime object detection algorithm based on the improved convolutional neural network (CNN). Firstly, a differential-evolutionary-based K-means (DK-means) anchor box clustering algorithm is proposed to obtain adaptive anchor boxes to satisfy the maritime object characteristics. Secondly, an adaptive spatial feature fusion (ASFF) module is added in the neck network to enhance multi-scale feature fusion. Finally, focal loss and efficient intersection over union (IoU) loss are adopted to replace the original loss function to improve the network convergence speed. The experimental results on the Singapore maritime dataset show that our proposed algorithm improves the average precision by 7.1%, achieving 72.7%, with a detection speed of 113 frames per second, compared with You Only Look Once v5 small (YOLOv5s). Moreover, compared to other counterparts, it can achieve a better speed–accuracy balance, which is superior and feasible for the complex maritime environment.

Static object detection for video surveillance

Static object detection for video surveillance

Object Detection in Video Surveillance Based on Multiscale Frame Representation and Block Processing by a Convolutional Neural Network

Object Detection in Video Surveillance Based on Multiscale Frame Representation and Block Processing by a Convolutional Neural Network

Abandoned Object Detection in Video-Surveillance: Survey and Comparison.

During the last few years, abandoned object detection has emerged as a hot topic in the video-surveillance community. As a consequence, a myriad of systems has been proposed for automatic monitoring of public and private places, while addressing several challenges affecting detection performance. Due to the complexity of these systems, researchers often address independently the different analysis stages such as foreground segmentation, stationary object detection, and abandonment validation. Despite the improvements achieved for each stage, the advances are rarely applied to the full pipeline, and therefore, the impact of each stage of improvement on the overall system performance has not been studied. In this paper, we formalize the framework employed by systems for abandoned object detection and provide an extensive review of state-of-the-art approaches for each stage. We also build a multi-configuration system allowing one to select a range of alternatives for each stage with the objective of determining the combination achieving the best performance. This multi-configuration is made available online to the research community. We perform an extensive evaluation by gathering a heterogeneous dataset from existing data. Such a dataset allows considering multiple and different scenarios, whereas presenting various challenges such as illumination changes, shadows, and a high density of moving objects, unlike existing literature focusing on a few sequences. The experimental results identify the most effective configurations and highlight design choices favoring robustness to errors. Moreover, we validated such an optimal configuration on additional datasets not previously considered. We conclude the paper by discussing open research challenges arising from the experimental comparison.

Foreground object detection for video surveillance by fuzzy logic based estimation of pixel illumination states

Foreground object detection for video surveillance by fuzzy logic based estimation of pixel illumination states

Histogram-based adaptive learning for background modelling: moving object detection in video surveillance

Histogram-based adaptive learning for background modelling: moving object detection in video surveillance

Histogram-based adaptive learning for background modelling: moving object detection in video surveillance

The detection of moving object in the presence of complex or cluttered background is a very critical challenge. The moving object may be a person, patient, vehicle, animal or any tissue inside body in medical domain. In this context, this work has proposed a robust background subtraction method for resolving illumination variation and motion-based problems. Initially, this work has developed a background modelling method using initial few frames in training stage. In testing stage, a foreground modelling method is investigated that is able to detect moving object in video frames. In testing stage, this work classify moving pixel with a suitable threshold and update the background using appropriate learning rate. The learning rate is updated through histogram of classified resulting frame and background model. Finally, morphological filters and image processing techniques are applied to improve the detection quality. The employed method also demonstrates how it can be improved using adaptive learning rate-based controlling scheme and the incorporation of feedback-based model updating scheme. It clearly depicts strength of proposed method in handling illumination variation problems and also eliminating moving environmental effects. This method presents significant performance in comparison with considered state-of-the-art methods.

Real time moving object detection for video surveillance based on improved GMM

Real time moving object detection for video surveillance based on improved GMM

Robust skin color-based moving object detection for video surveillance

Robust skin color-based moving object detection for video surveillance is proposed. The objective of the proposed algorithm is to detect and track the target under complex situations. The proposed framework comprises four stages, which include preprocessing, skin color-based feature detection, feature classification, and target localization and tracking. In the preprocessing stage, the input image frame is smoothed using averaging filter and transformed into YCrCb color space. In skin color detection, skin color regions are detected using Otsu’s method of global thresholding. In the feature classification, histograms of both skin and nonskin regions are constructed and the features are classified into foregrounds and backgrounds based on Bayesian skin color classifier. The foreground skin regions are localized by a connected component labeling process. Finally, the localized foreground skin regions are confirmed as a target by verifying the region properties, and nontarget regions are rejected using the Euler method. At last, the target is tracked by enclosing the bounding box around the target region in all video frames. The experiment was conducted on various publicly available data sets and the performance was evaluated with baseline methods. It evidently shows that the proposed algorithm works well against slowly varying illumination, target rotations, scaling, fast, and abrupt motion changes.

Moving object detection for video surveillance.

The emergence of video surveillance is the most promising solution for people living independently in their home. Recently several contributions for video surveillance have been proposed. However, a robust video surveillance algorithm is still a challenging task because of illumination changes, rapid variations in target appearance, similar nontarget objects in background, and occlusions. In this paper, a novel approach of object detection for video surveillance is presented. The proposed algorithm consists of various steps including video compression, object detection, and object localization. In video compression, the input video frames are compressed with the help of two-dimensional discrete cosine transform (2D DCT) to achieve less storage requirements. In object detection, key feature points are detected by computing the statistical correlation and the matching feature points are classified into foreground and background based on the Bayesian rule. Finally, the foreground feature points are localized in successive video frames by embedding the maximum likelihood feature points over the input video frames. Various frame based surveillance metrics are employed to evaluate the proposed approach. Experimental results and comparative study clearly depict the effectiveness of the proposed approach.

面向多区域视频监控的运动目标检测系统

面向多区域视频监控的运动目标检测系统

A robust adaptive algorithm of moving object detection for video surveillance

In visual surveillance of both humans and vehicles, a video stream is processed to characterize the events of interest through the detection of moving objects in each frame. The majority of errors in higher-level tasks such as tracking are often due to false detection. In this paper, a novel method is introduced for the detection of moving objects in surveillance applications which combines adaptive filtering technique with the Bayesian change detection algorithm. In proposed method, an adaptive structure firstly detects the edges of motion objects. Then, Bayesian algorithm corrects the shape of detected objects. The proposed method exhibits considerable robustness against noise, shadows, illumination changes, and repeated motions in the background compared to earlier works. In the proposed algorithm, no prior information about foreground and background is required and the motion detection is performed in an adaptive scheme. Besides, it is shown that the proposed algorithm is computationally efficient so that it can be easily implemented for online surveillance systems as well as similar applications.

Robust and accurate moving shadow detection based on multiple features fusion

Abstract In recent years, moving cast shadow detection has been becoming a critical challenge to improve the accuracy of moving object detection in video surveillance. In this paper, we derive a robust moving cast shadow detection method based on multiple features fusion. Firstly, several kinds of features such as intensity, color and texture are extracted sufficiently by means of various measures for the foreground image. Then, the synthetic feature map is generated by linear combination of these features. Consequently, moving cast shadow pixels are distinguished from their moving objects roughly. Finally, spatial adjustment is applied to correct misclassified pixels for acquiring the refined shadow detection result. The effectiveness of our proposed method is evaluated on various scenes. The results demonstrate that the method can achieve high detection rate. In particular, the experiments also indicate that it significantly outperforms several state-of-the-art methods by extensive comparisons.

Addressing uncertainty in multi-modal fusion for improved object detection in dynamic environment

A major challenge in real world object detection for Video Surveillance (VS) is the dynamic nature of environmental conditions with respect to illumination, visibility, weather change, etc. With increasing availability of cameras and other sensor modalities beyond visible spectrum at lower cost, multi-modal VS systems involving visible, thermal infrared cameras, etc., are seen as a promising solution for reliable and robust operation in unfavorable environmental conditions like at night or dark situation. However, there are several research challenges to actually utilize the combined benefits of using different modalities. This paper addresses the uncertainty problem in the fusion of the information provided by complementary modalities like visible spectrum and thermal infrared video in a generic framework using evidence theory. A belief model is developed to determine the validity of a foreground region detected by each source for tracking. Fuzzy logic modeling is done for generating belief mass function from the sensor information by using two measurement features. A novel algorithm is developed for dynamic assessment of individual sensor reliability within the belief model. A generic approach to re-assign the conflicting mass is adopted for belief fusion to be done in a weighted manner depending on the context. Finally, the confirmed objects are tracked and the sensor measurements of their position, size, etc., are fused using a weighted Kalman filter fusion method. The approach is evaluated by using a pair of visible and thermal infrared sensors in real world challenging scenarios.

Adaptive background subtraction based on feedback from fuzzy classification

Background subtraction is an important issue for achieving effective foreground object detection in video surveillance. Background subtraction requires the timely updating of a background model to gradual illumination changes as well as the significant changes in the background. It is also essential that foreground objects have little impact on the updating of the background. Based on our change-type categories, we propose an adaptive background subtraction method where a two-strategy-based background maintenance is introduced to adapt to different types of changes by using feedback from change segmentation and region classification. The work mainly contributes to the following issues: 1. propose a change segmentation method that detects change regions as well as provides spatiotemporal information about the changes by using fuzzy techniques; 2. propose a fuzzy reasoning method to classify background and foreground changes at the object level; and 3. propose a new method for adaptive background maintenance based on the feedback from pixel-level to object-level processing that is able to avoid tradeoff in the updating rate. Experiments on indoor and outdoor video scenes are conducted and results show that the proposed method adapts well to various background changes without absorbing foreground objects. Comparisons with an existing method using a constant learning rate show an improved performance.