Moving Object Detection and Tracking Algorithm Based on Background Subtraction

High tracking frame rates have been achieved based on traditional tracking methods which however would fail due to drifts of the object template or model, especially when the object disappears from the camera’s field of view. To deal with it, tracking-and-detection-combination has become more and more popular for long-term unknown object tracking, whose detector almost does not drift and can regain the disappeared object when it comes back. However, for online machine learning and multiscale object detection, expensive computing resources and time are required. So it is not a good idea to combine tracking and detection sequentially like Tracking-Learning-Detection algorithm. Inspired from parallel tracking and mapping, this article proposes a framework of parallel tracking and detection for unknown object tracking. The object tracking algorithm is split into two separate tasks—tracking and detection which can be processed in two different threads, respectively. One thread is used to deal with the tracking between consecutive frames with a high processing speed. The other thread runs online learning algorithms to construct a discriminative model for object detection. Using our proposed framework, high tracking frame rates and the ability of correcting and recovering the failed tracker can be combined effectively. Furthermore, our framework provides open interfaces to integrate state-of-the-art object tracking and detection algorithms. We carry out an evaluation of several popular tracking and detection algorithms using the proposed framework. The experimental results show that different tracking and detection algorithms can be integrated and compared effectively by our proposed framework, and robust and fast long-term object tracking can be realized.

Moving Object Detection systems have long been in use to look at security sensitive areas. The manufacture of video investigating systems sensible desires fast, reliable and durable algorithms for moving object detection classification pursuit and activity analysis. Moving object detection is that the essential step for extra analysis of video. It handles segmentation of moving objects from stationary background objects. Object classification step categorizes detected objects into preened classes like human, vehicle, animal, clutter, etc. it's a necessity to inform apart objects from each other therefore on trace and analyse their actions reliably. In previous system we have got performed background subtraction by victimization clever Edge Detection. In clever Edge Detection technique we tend to tend to square measure taking 2 photos for comparison those square measure background image and foreground image. Previous strategies for object detection square measure methodology along side object detectors image segmentation and Background subtraction. Our methodology aims to section objects supported motion info and it contains a part of background modelling. In the previous strategies we tend to tend to square measure conducting background subtraction only for photos. For this we tend to tend to projected an image component wise background modelling and subtraction technique mistreatment multiple choices. Hence, throughout this color, gradient and Hear-Like choices square measure integrated to handle the variation element. Thus, motion segmentation and background subtraction square measure the foremost connected topics to the current paper.

The paper presents review of key aspects associated with object detection and tracking algorithms in sensing systems for automotive applications covering active safety, advanced driver assistance and autonomous driving systems. The role of discussed systems is to establish location and monitor relative movement of objects and environment with main focus on identification of vulnerable road users. The object detection algorithms allow to classify and differentiate both static and dynamic objects in the vehicle’s surrounding. The analysis will be focused on selection of most suitable platform for development of optimized and safe vulnerable road user detection system.

As an important part of intelligent vehicle, environmental perception system mainly refers to the detection of the surrounding environment of the vehicle by the sensors attached on the vehicle. In order to ensure the accuracy and stability of the intelligent vehicle environmental perception system, it is necessary to use intelligent vehicle sensors to detect and track objects in the passable area. In this paper, an object detection and tracking algorithm based on the LiDAR and camera information fusion is proposed. The algorithm uses the point cloud data clustering method of LiDAR to detect the objects in the passable area and project them onto the image to determine the tracking objects. After the objects are determined, the algorithm uses color information to track objects in the image sequence. Since the object tracking algorithm based on image is easily affected by light, shadow and background interference, the algorithm uses LiDAR point cloud to modify the tracking results. This paper uses KITTI data set to verify and test this algorithm and experiments show that the target area detection overlap of the proposed target detection and tracking algorithm is 83.10% on average and the tracking success rate is 80.57%. Compared with particle filtering algorithm, the average region overlap increased by 29.47% and the tracking success rate increased by 19.96%.

Object detection and tracking has always been one of the important research directions in computer vision. The purpose is to determine whether the object is contained in the input image and enclose the object with a bounding box. However, most object detection and tracking methods are applied to daytime objects, and the processing of nighttime objects is imprecise. In this paper, a spectral-spatial feature enhancement algorithm for nighttime object detection and tracking is proposed, which is inspired by symmetrical neural networks. The proposed method consists of the following steps. First, preprocessing is performed on unlabeled nighttime images, including low-light enhancement, object detection, and dynamic programming. Second, object features for daytime and nighttime times are extracted and modulated with a domain-adaptive structure. Third, the Siamese network can make full use of daytime and nighttime object features, which is trained as a tracker by the above images. Fourth, the test set is subjected to feature enhancement and then input to the tracker to obtain the final detection and tracking results. The feature enhancement step includes low-light enhancement and Gabor filtering. The spatial-spectral features of the target are fully extracted in this step. The NAT2021 dataset is used in the experiments. Six methods are employed as comparisons. Multiple judgment indicators were used to analyze the research results. The experimental results show that the method achieves excellent detection and tracking performance.

Performance evaluation of selected complex video processing algorithms, implemented on a parallel, embedded GPU platform Tegra X1, is presented. Three algorithms were chosen for evaluation: a GMM-based object detection algorithm, a particle filter tracking algorithm and an optical flow based algorithm devoted to people counting in a crowd flow. The choice of these algorithms was based on their computational complexity and parallel structure. The aim of the experiments was to assess whether the current generation of low-power, mobile GPUs has sufficient power for running live analysis of video surveillance streams, e.g. in smart cameras, while maintaining energy consumption at a reasonable level. Tests were performed with both a synthetic benchmark and a real video surveillance recording. It was found that the computational power of the tested platform is sufficient for running operations such as background subtraction, but in case of more complex algorithms, such as tracking with particle filters, performance is not satisfactory because of inefficient memory architecture which stalls the processing.

Event cameras utilize novel imaging sensors patterned after visual pathways in the brain that are responsive to low contrast, transient events. Specifically, the pixels of dynamic vision sensors (DVS) react independently and asynchronously to changes in light intensity, creating a stream of time-stamped events encoding the pixels’ (x, y) location in the sensor array and the sign of the brightness change. In contrast with conventional cameras that sample every pixel at a fixed rate, DVS pixels produce output only when the change in intensity has surpassed a set threshold, which leads to reduced power consumption in scenes with relatively little motion. Furthermore, compared to conventional CMOS imaging pixels, DVS pixels have extremely high dynamic range, low latency, and low motion blur. Taken together, these characteristics make event cameras uniquely qualified for persistent surveillance. In particular, we have been investigating their use in port surveillance applications. Such an application of DVS presents the need for automated pattern recognition and object tracking algorithms which can process event data. Due to the fundamentally different nature of the output relative to conventional frame-based cameras, traditional methods of machine learning for computer vision cannot be directly applied. Anticipating this need, this work details data collection and collation efforts to facilitate development of object detection and tracking algorithms in this modality. We have assembled a maritime dataset capturing several moving objects including sail boats, motor boats, large ships, etc.; as well as incidentally captured objects. The data was collected with lenses of various focal lengths and aperture settings to provide data variability and avoid unwanted bias to specific sensor parameters. In addition, the captured data was recorded with the camera in both static and dynamic states. These different states can be used to mimic potential behavior and help understand how this movement can affect the algorithms being developed for automated ship detection and tracking. We will describe the data captured, effects of hardware settings and lenses, as well as how lighting conditions and sensor movement contributed to the quality of the event data recorded. Finally, we will detail future data collection efforts.

Today, Computer Vision is one of the most well-liked and critical research areas in the Computer Science field. Moving object detection is the first and foremost step in any computer vision applications. The detection of moving object in any computer vision application is a very critical task. The most widely used method for moving object detection is Frame differencing based background subtraction method. The most of the researchers address the various challenges of background subtraction algorithm to detect the object accurately, the segmentation of objects from non stationary objects is critical, adaptation of illumination changes and handling of camouflage and etc. But they are more CPU and I/O intensive one. Cloud Computing is the new buzz word in computer field which provides computational power on demand basis which requires the representation of e-science workflow model for such kind of large-scale scientific applications. There are four different types of scientific work flow models viz Montage, LIGO, SIPHT, and Cybershake are exist which are varied by either CPU intensive or I/O intensive application. But the Temporal frame differencing based real time object detection is both CPU and I/O intensive one. Hence which could not be expresses as a single workflow model for cloud computing. So, we proposed a hybrid workflow model (Montage and SIPHT) of temporal frame differencing algorithm for real time object detection and the performance of such workflow model is tested and analyzed in already existing scheduling algorithm in workflowsim.

Most conventional object tracking algorithms are implemented on general-purpose processors in software due to its great flexibility. However, the real-time performance is hard to achieve due to the inherent characteristics of the sequential processing of these processors. To tackle this issue, a reconfigurable system-on-chip (rSoC) platform with microprocessors and FPGAs is applied in this paper. To simplify the hardware/software interface, a Belief–Desire–Intention (BDI)-based multi-agent architecture is proposed as the unified framework. Then an agent-based task graph and two heuristic partitioning methods are proposed to partition the hardware and software on an rSoC platform. Compared to the module-based architecture, this BDI-based multi-agent architecture provides more efficiency, flexibility, autonomy, and scalability for the real-time tracking systems. A particle swarm optimization (PSO)-based object detection and tracking algorithm is applied to evaluate the proposed architecture. Extensive experimental results of object tracking demonstrate that the proposed architecture is efficient and highly robust with real-time performance.

The rapid advancement in the field of deep learning and high performance computing has highly augmented the scope of video-based vehicle counting system. In this paper, the authors deploy several state-of-the-art object detection and tracking algorithms to detect and track different classes of vehicles in their regions of interest (ROI). The goal of correctly detecting and tracking vehicles’ in their ROI is to obtain an accurate vehicle count. Multiple combinations of object detection models coupled with different tracking systems are applied to access the best vehicle counting framework. The models’ addresses challenges associated to different weather conditions, occlusion and low-light settings and efficiently extracts vehicle information and trajectories through its computationally rich training and feedback cycles. The automatic vehicle counts resulting from all the model combinations are validated and compared against the manually counted ground truths of over 9 h’ traffic video data obtained from the Louisiana Department of Transportation and Development. Experimental results demonstrate that the combination of CenterNet and Deep SORT, and YOLOv4 and Deep SORT produced the best overall counting percentage for all vehicles.

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).

Object detection and tracking has been gaining widespread interest and significance with rate of increase in technology towards development of new gadgets. From a continuous video locating a particular object and tracking it is a sequence of process which involves segmentation, preprocessing, extracting the features, finally clustering for recognizing the particular object. This research works highlights the maximum capability of results in order to detect and track the object using a set of algorithms for detection mixed along with the optimization algorithms for better computation time and minimum of errors. The proposed work exploits the contour extraction followed by computation of dissimilarity measure between two signals. A single video sequence partitioned into frames has been optimized for an efficient tracking as evident from the end results.

This paper presents an investigation of the development of a real-time object tracking system that utilizes computer vision techniques and incorporates pan-tilt control mechanisms. The system architecture includes hardware components such as a camera and a pan-tilt mechanism, and software components such as object detection and tracking algorithms. To achieve efficient and accurate tracking, the Kalman filter, which uses a state-space model, was included in the object tracking process. The proposed system is implemented and evaluated through experiments to demonstrate its feasibility and effectiveness in real-world scenarios. The results indicate that the proposed system is capable of achieving real-time and accurate object tracking, demonstrating its potential for practical applications in various fields.

With the increasing number of drones, the danger of their illegal use has become relevant. This has necessitated the creation of automatic drone protection systems. One of the important tasks solved by these systems is the reliable detection of drones near guarded objects. This problem can be solved using various methods. From the point of view of the price–quality ratio, the use of video cameras for a drone detection is of great interest. However, drone detection using visual information is hampered by the large similarity of drones to other objects, such as birds or airplanes. In addition, drones can reach very high speeds, so detection should be done in real time. This paper addresses the problem of real-time drone detection with high accuracy. We divided the drone detection task into two separate tasks: the detection of moving objects and the classification of the detected object into drone, bird, and background. The moving object detection is based on background subtraction, while classification is performed using a convolutional neural network (CNN). The experimental results showed that the proposed approach can achieve an accuracy comparable to existing approaches at high processing speed. We also concluded that the main limitation of our detector is the dependence of its performance on the presence of a moving background.

Moving object detection and feature extraction algorithm in video sequences are discussed in this paper. There are several problems in moving object detection and extraction from outdoor video surveillance, that is, moving object detection algorithm is easily interfered by background of video monitor, the feature of moving object is difficult to extract from video source, and the vibration of picture frame in outdoor video surveillance causing by wind factors effects the incorrect extraction of the moving object. The vibration causing by wind factors was corrected and an enhancement inter-frame difference algorithm based on difference histogram threshold selection is presented in this paper. The experiment results prove that this method can detect and extract the moving object accurately and efficiently, and it can meet the needs of real-time detection.