Moving object detection with moving background using optic flow

Moving object detection plays a key role in video surveillance. A number of object detection methods have been proposed in the spatial domain. In this study, the authors propose a compressed sensing-based algorithm for the detection of moving object. They first use a practical three-dimensional circulant sampling method to yield sampled measurements. Then, they propose an object detection model to simultaneously reconstruct the foreground support, background and video sequence using the sampled measurements directly. Experimental results show that the proposed moving object detection algorithm outperforms the state-of-the-art approaches and it is robust to the movement turbulence, camera motion and video noise.

Moving object detection and tracking from video captured by moving camera

Recent advances in the field of object detection and face recognition have made it possible to develop practical video surveillance systems with embedded object detection and face recognition functionalities that are accurate and fast enough for commercial uses. In this paper, we compare some of the latest approaches to object detection and face recognition and provide reasons why they may or may not be amongst the best to be used in video surveillance applications in terms of both accuracy and speed. It is discovered that Faster R-CNN with Inception ResNet V2 is able to achieve some of the best accuracies while maintaining real-time rates. Single Shot Detector (SSD) with MobileNet, on the other hand, is incredibly fast and still accurate enough for most applications. As for face recognition, FaceNet with Multi-task Cascaded Convolutional Networks (MTCNN) achieves higher accuracy than advances such as DeepFace and DeepID2+ while being faster. An end-to-end video surveillance system is also proposed which could be used as a starting point for more complex systems. Various experiments have also been attempted on trained models with observations explained in detail. We finish by discussing video object detection and video salient object detection approaches which could potentially be used as future improvements to the proposed system.

Detection of objects precisely in video surveillance system is vital for many applications such as abnormal activity detection, congestion analysis, person identification, military fields. For detection of objects, background subtraction and temporal detection methods are used. In background subtraction method moving and static objects which enters into a camera viewing region has been detected. In this method reference frame will be captured during camera initialization. It uses a reference frame as background, detects present frame and takes the difference between two frames. In this paper objects are detected in multiple regions separately from single camera instead of using multiple cameras to monitor different objects in different regions. And also user will have control to select required single or multiple regions to detect the object. This will reduces the overall cost of the system and increases the processing speed of the system if we choose a particular region. Accuracy of the object detection in different regions increases since it allows using different parameters like thresholding separately. So that it monitors different activities at the same time. Also detection of objects based on comparison, object of interest from stored data base is presented using single static camera view with good real time performance.

In real time object tracking is an important task in various surveillance application. Nowadays, surveillance system very common in offices, collage campus, shopping malls etc. In this paper, an automated video surveillance system presented. Althrough tracking and counting system are commercially available today; there is need for further reaserach to address the challenges of real world scenarios. There is lot of surveillance cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop a computer vision based technologies that automatically process those images in order to detect situations or unusual behavior. In order to solve the problem of poor real time tracking performance using convolutional neural network. This paper proposes a fast and accurate real time video detection and tracking algorithm. Keyword: - Video surveillance system, Moving object detection, Tracking, Background subtraction. I. Introduction Real time human body detection and tracking in indoor environment deals with the concept of higher level application of image processing and computer vision. The following key components of identify to path of real time tracking are extracting a feature, background image subtraction. If two similar color objects come to close the tracker of one object can jump to associated object. The problem is called hijacking problem. Drifting problem occur when an object abruptly changes its direction to reverse. In that case it’s become very difficult to track the object because the motion model does not work. An object tracking can be understood as the problem of finding the path (i.e. Trajectory).

Nowadays, video surveillance systems apply video compression to reduce bandwith and storage cost. However, generally, these video sequences are the input, after decoding, for video analysis modules. H.264/AVC is the newest video standard and is assumed to be omnipresent in video surveillance systems in the near future. Since the video compression introduces artefacts, which influence the performance of these analysis modules, it is important to make a quantitative evaluation of this effect. Hence, in this paper we present the first quantitative analysis of the effect that H.264/AVC compression has upon a generally accepted moving object detection technique. We analyze different encoding schemes and show the influence on the object detection results for different representative sequences.

To detect moving objects is a difficult task for various image processing and computer vision applications viz., motion segmentation, object classification and identification, behavior understanding, event detection, object tracking and object locating. The process becomes even more difficult with moving camera as compared to static camera as both camera and object motions are combined in the detection process. Moreover, almost all real time video sequences incorporate pan/tilt/zoom camera which makes it essential to detect objects in moving cameras. This paper presents a survey of various moving object detection techniques in moving cameras and gives insight picture of the methods like background subtraction, optical flow, feature based object detection and blob analysis. It also mentions pros and cons of every technique used so far individually. The use of these approaches and progress made over years has been tracked and elaborated.

To detect moving objects from a video sequence is a fundamental and critical task in many computer vision applications. For China high-speed railway transport hub video surveillance system, we need a stable, fast and accurate moving object detection method to promptly find the congestion of passenger flow and other dangerous in hub. Through the comparative study on moving object detection, we find the average/median filtering is the most appropriate method for China high-speed railway transport hub video surveillance.

In this paper, we proposed a novel video enhancement for moving-objects in the dusk environment based on mixed-mode processing which involves de-noising, moving object detection, and image enhancement. The basic strategy is to first calculate each pixel's motion vector and then check the magnitude of this motion vector to determine that the pixel belongs to foreground (i.e., moving object) or background. Then, the pixel of foreground is processed with the temporal average filter to further check if it belongs to the moving object by motion detection. Finally, the extracted moving object is enhanced by using Bilateral filter and Retinex algorithm. Experimental results show that the proposed method is able to divide the frame into foreground and background and the foreground has a significantly clear and high contrast appearance in the low-light environment.

The world is marching towards digital communication and digital video surveillance is an indispensable part of it. Moving object detection and face detection play an important role in the video surveillance applications. It is a part of security oriented applications such as traffic systems, hospitals, banks. Background subtraction algorithm is used in order to detect the foreground. Due to the light intensity variations in dynamically changing environment, background subtraction module needs an adaptive threshold value for each input video frame. The pixel values above the threshold value are detected as foreground pixels. The range of adaptive threshold values usually varies from 10 to 85 in the proposed work. This gives a better detection capability in moving object detection wherein PSNR of 32.39 is obtained when two detected images with and without adaptive threshold are chosen as inputs to the PSNR block. Face detection is treated as an extension module of moving object detection wherein the input face image is subtracted from the test image with the help of adaptive threshold but finally the matching unit is the additional component which decides the match based on the global threshold value which is chosen as 183000 here. Here, instead of detecting the moving body, only face is considered for detection. Since real time videos need to be processed using FPGA kits, an area efficient moving object and face detection system is designed using Spartan 6 FPGA kit. The number of slice registers used has reduced from 365 to 320 in case of moving object detection, from 861 to 537 in case of face detection. The results obtained reveal that the use of adaptive threshold concept results in better detection process with PSNR of 32.39.

Unmanned Aerial Vehicles (UAVs) equipped with video cameras are a flexible support to ensure civil and military safety and security. In this thesis, a video processing chain is presented for moving object detection in aerial video surveillance. A Track-Before-Detect (TBD) algorithm is applied to detect motion that is independent of the camera motion. Novel robust and fast object detection and segmentation approaches improve the baseline TBD and outperform current state-of-the-art methods.

In order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection. It delves into the complexities of numerous moving objects, dense targets, and intricate backgrounds. Leveraging the You Only Look Once (YOLOv3) algorithm framework, this paper proposes improvements in image segmentation and data filtering to address these challenges. These enhancements form a novel multi-object detection algorithm based on an improved YOLOv3 framework, specifically designed for video applications. Experimental validation demonstrates the feasibility of this algorithm, with success rates exceeding 60% for videos such as “jogging”, “subway”, “video 1”, and “video 2”. Notably, the detection success rates for “jogging” and “video 1” consistently surpass 80%, indicating outstanding detection performance. Although the accuracy slightly decreases for “Bolt” and “Walking2”, success rates still hover around 70%. Comparative analysis with other algorithms reveals that this method’s tracking accuracy surpasses that of particle filters, Discriminative Scale Space Tracker (DSST), and Scale Adaptive Multiple Features (SAMF) algorithms, with an accuracy of 0.822. This indicates superior overall performance in target tracking. Therefore, the improved YOLOv3-based multi-object detection and tracking algorithm demonstrates robust filtering and detection capabilities in noise-resistant experiments, making it highly suitable for various detection tasks in practical applications. It can address inherent limitations such as missed detections, false positives, and imprecise localization. These improvements significantly enhance the efficiency and accuracy of target detection, providing valuable insights for researchers in the field of object detection, tracking, and recognition in video surveillance.

Automatic moving object detection is essential for various computer vision applications like video surveillance systems. Many previous detection methods work for usually low-res video sequences under certain constraints and are based on background learning and/or pixel-level motion analysis or they focus on detecting particular objects. We introduce a hybrid moving object detection scheme with motion-color features, followed by a statistical optimization step to increase the accuracy of the boundaries of the detected objects in hi-resolution video sequences taken in the presence of camera motions such as camera vibrations. Motion analysis involves both the motion vector information extracted from a reference H.264 decoder and a moving-edge map in order to produce an overestimate of the moving object blobs. Pyramid color segmentation connecting multiple components that might be under different motions is performed to extract the solid bodies of moving object blobs with accurate boundaries. Results from motion and color analysis are fused and a region-growing technique based on the blobs' Gaussian distribution of its RGB information is performed to further refine moving blobs. Results are shown to demonstrate the accuracy of our method.

Foreground detection is a prime task in the field of computer vision for targeting the emerging applications like video surveillance, object tracking, action recognition, scene analysis. For moving object detection, it is always desirable to accurately extract the foreground under complex background conditions with less computational overhead. In this work, we propose a multifeature-based moving object detection scheme, where the feature vector for each pixel constitutes gray level intensity value and extended scale-invariant local ternary pattern (E-SILTP) over a local region. Further, to improve the detection accuracy with minimum computational cost, extended Canberra distance is employed for similarity distance between model and current pixel instead of popular Mahalanobis distance and Forstner distance. The experimental results are validated using some standard data sets and shows superior performance than that of the benchmark schemes.

This study is concerned with detection of objects in motion from time serial pictures of a 6-D of freedom motion monocular camera. As a point in real space throw an image on corresponding pixels of time serial pictures. So to solve this problem to find out the corresponding algorithm is essential. In this paper we proposed a new method of the detection of motion objects based on compensating background image shift caused by camera rotation and translation. The main point of this idea is, to take three pictures by the passive sensor (CCD camera) in three different times tk, tk+τ1 and tk+τ2 while the platform is moving, by correcting them rotationally (also called vertical rectification) the rotation disparity will be excluded, and we get three parallel frames that will be matched through their geometrical constraint for the translation-distortion extraction.