A fast active contour algorithm for object tracking in complex background

3D tracking of objects and hands in an object manipulation scenario is a very interesting computer vision problem with a wide variety of applications ranging from consumer electronics to robotics and medicine. Recent advances in this research topic allow for 3D tracking of complex scenarios involving bi-manual manipulation of several rigid objects using commodity hardware and with high accuracy. The problem with these approaches is that they treat tracking as a search problem whose dimensionality increases with the number of objects in the scene. This fact typically limits the number of the tracked objects and/or the processing framerate. In this paper we present a method that utilizes simple low level motion cues for dynamically assigning computational resources to parts of the scene where they are actually required. In a series of experiments, we show that this simple idea improves tracking performance dramatically at a cost of only a minor degradation of tracking accuracy. The works that are most related to ours are the approaches by Kyriazis and Argyros on top-down 3D tracking of multiple active objects from RGBD input [1, 2]. The methodological part of our contribution can be briefly described as an extra processing node in the pipeline of [2] which it extends. The tracking approach in [2], the Ensemble of Collaborative Trackers (ECT), regards a set of semi-independent trackers. Each tracker is associated with a distinct object in the scene. For an object to be tracked, a separate optimization problem is solved, one for each frame and each object. Each optimization problem is numerically solved using a black box optimizer, i.e. a variant of the Particle Swarm Optimization (PSO) algorithm. PSO treats the objective function as an oracle and queries it on purposefully evolved “guesses” in the search space, in order to find the optimum. The harder the problem is, the more guesses (budget, which is the product of PSO particles and PSO generations that need to be computed) are required for adequate accuracy to be achieved. For example, tracking the pose and the articulation of the hand amounts to solving a 27-parameter optimization problem. This is much harder than solving for the 3D pose of a rigid object (6 parameters). ECT [2] assigns a fixed amount of computational resources to each tracking sub-problem which depends on this notion of complexity and which is empirically estimated. In this work, we quantify at run time, how hard the tracking of an object should be, not only based on its intrinsic complexity, but also based on its observed dynamics. An object that appears static in the recent temporal window requires less resources compared to an object whose state is more dynamic. Thus, change detection is performed on the image space of color intensities and depth measurements of the RGBD input. In more detail, for the next tracking frame, and the ith tracker, a value mi is computed, which takes the value of 0 if the corresponding tracked object appears to be relatively static, and takes the value of 1 otherwise. For mi to be 1, any of the following need to be true: • the mean value of the pixel-wise differences between the observations of object i and the back-projection of the last estimated configuration of object i is high enough, • the kinetic energy of object i, as computed from so far tracked velocities, suggests a moving object, • the amount of missing depth measurements changes substantially, from one frame to the next, which might me attributed to change in the slant of object i, or • any of the above was satisfied in the recent past (damping). Budget has the trivial minimum value of 0. Moreover, as it has been shown in [3, 4], a PSO budget of 64 particles and 64 generations suffices to track a hand, even in interaction with another hand. No more budget should be required to track simpler structures such as rigid objects. The proposed dynamic budget allocation policy assigns a minimum budget Bmin of 64 particles running for 4 generations to the objects that are static and a maximum budget Bmax that never exceeds the aforementioned (a) Pouring pancake mix (b) Waiting for the mix to cook

In this letter, we propose a fast mirror-drive pan-tilt target tracking system that can robustly track an object whose appearance varies in a complex background at 500 fps. By assuming a small image displacement between frames, which is a property of high-frame rate vision, we develop an fast object tracking algorithm by hybridizing the convolutional-neural-network (CNN) based object detection with template-matching (TM) based tracking operating at hundreds of frames per second (fps). For object tracking with high-speed visual feedback, the proposed tracking algorithm can remarkably reduce dozens-of-milliseconds-latency in the CNN-based object detection by simultaneously executing TM-based tracking for several images at consecutive frames within a few milliseconds. In the proposed pan-tilt tracking system, when the current tracked objects are occluded or out of the camera view, it can recognize objects to be newly tracked with CNN-based object detection at the rate of 33 fps with acceleration using graphic processing units (GPUs). Controlling the pan-tilt tracking system via visual feedback at 500 Hz, fast moving objects can be robustly tracked at the center of the camera view. The effectiveness of our method was experimentally demonstrated via several results when fast-moving pre-learned objects, such as toy cars were tracked in complex backgrounds.

We present experiments to illustrate tracking of occluded objects using three-dimensional (3D) integral imaging (II). Tracking of heavily occluded objects by conventional two-dimensional (2D) image processing may be difficult owing to the superposition of occlusion noise and object details. The effects of occlusion are remedied by 3D computational II reconstruction. We use a summation of absolute difference (SAD) algorithm between pixels of consecutive frames of a moving object for 3D tracking. SAD algorithm is not robust for 2D images of occluded objects; 3D computation reconstruction of the scene allows implementation of a SAD algorithm by reducing occlusion effects. Experimental results demonstrate 3D tracking of occluded objects. To the best of our knowledge, this is the first report on 3D tracking of objects using II.

BackgroundInspection of livestock farms using surveillance cameras is emerging as a means of early detection of transboundary animal disease such as African swine fever (ASF). Object tracking, a developing technology derived from object detection aims to the consistent identification of individual objects in farms.ObjectivesThis study was conducted as a preliminary investigation for practical application to livestock farms. With the use of a high-performance artificial intelligence (AI)-based 3D depth camera, the aim is to establish a pathway for utilizing AI models to perform advanced object tracking.MethodsMultiple crossovers by two humans will be simulated to investigate the potential of object tracking. Inspection of consistent identification will be the evidence of object tracking after crossing over. Two AI models, a fast model and an accurate model, were tested and compared with regard to their object tracking performance in 3D. Finally, the recording of pig pen was also processed with aforementioned AI model to test the possibility of 3D object detection.ResultsBoth AI successfully processed and provided a 3D bounding box, identification number, and distance away from camera for each individual human. The accurate detection model had better evidence than the fast detection model on 3D object tracking and showed the potential application onto pigs as a livestock.ConclusionsPreparing a custom dataset to train AI models in an appropriate farm is required for proper 3D object detection to operate object tracking for pigs at an ideal level. This will allow the farm to smoothly transit traditional methods to ASF-preventing precision livestock farming.

A review of object tracking methods: From general field to autonomous vehicles

The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems.

This paper presented a video moving object segmentation and tracking system based on the active contour and the color classification models. First, the active contour model is applied to segment the target object in the initial frame. From the segmented object, the object and background regions are extracted. Then the object and the background regions are separately clustered according to color feature by using the K-means algorithm. Subsequently, the video object in the next frame is automatically tracked by using temporal differencing and block matching. The moving and stationary regions in a frame are estimated by the temporal differencing. In the moving regions, pixels are obtained their classification from the previous frame using block matching while they are directly received their classification from the previous frame in the stationary regions. Experimental results show that the proposed method provides better performance than the active contour method applied in video object tracking.

Object tracking is an important basis for the autonomous navigation of unmanned surface vehicles. However, several problems still must be addressed for a wide applicating of object tracking in unmanned surface vehicles. First, if multiple objects of the same classification exist in the same field of view, then stable extraction of an object is difficult. Second, in an environment with a complex background and large changes in object shape, the tracking accuracy is low, and object tracking errors and tracking loss can easily occur. Third, much time is required to detect a high-resolution real-time video stream, not meeting the delay requirement of the photoelectric servo stable tracking. To resolve these problems, this paper proposes an object detection-tracking algorithm based on a radar-photoelectric system. The algorithm combines an object detection algorithm with an object tracking algorithm and involves the following steps. First, a first-frame object extraction algorithm is used to extract the tracking object from the first frame. Second, a region of interest (ROI)-prediction algorithm is used to predict ROIs and detect objects in these ROIs. This algorithm can effectively solve the above problems in marine tests. When multiple objects of the same classification exist in the same field of view, the algorithm can extract the radar-guided object stably. When faced with a complex background and a large change in object shape, the algorithm substantially improves the accuracy and robustness of object tracking. Compared with the conventional object detection algorithm, the time consumption of this algorithm is reduced by 25.8%.

Aiming at the existing problems of object tracking in real scenes, such as complex background, illumination changes, fast motion, and object rotation, the paper has proposed an object tracking algorithm via adaptive multi-feature fusion. By extracting the HOG feature of the object and using convolutional neural networks to extract high-level and low-level convolutional features, an adaptive threshold segmentation method has been used to evaluate the effect of each feature, and the weight ratio of feature fusion has been obtained. The response map of each feature has fused according to the weight coefficient, and the new estimated position of the object has been obtained, and the object scale has been calculated by the scale correlation filter, and the object scale has been obtained to complete the object tracking. The experimental results had conducted on the OTB-2013 dataset. The two-layer convolutional feature and the HOG feature are adaptively fused, so that the more discriminative single feature fusion weight is greater, which better expresses the appearance model of the object, and shows strong object tracking accuracy in scenes such as complex background, the disappearance of the object, light change, fast movement, and rotation.

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).

Improved foreign object tracking algorithm in coal for belt conveyor gangue selection robot with YOLOv7 and DeepSORT

A recent trend is to combine multiple sensors ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , cameras, LiDARs and millimeter-wave Radars) to achieve robust multi-modal perception for autonomous systems such as self-driving vehicles. Although quite a few sensor fusion algorithms have been proposed, some of which are top-ranked on various leaderboards, a systematic study on how to integrate these three types of sensors to develop effective multi-modal 3D object detection and tracking is still missing. Towards this end, we first study the strengths and weaknesses of each data modality carefully, and then compare several different fusion strategies to maximize their utility. Finally, based upon the lessons learnt, we propose a simple yet effective multi-modal 3D object detection and tracking framework (namely EZFusion). As demonstrated by extensive experiments on the nuScenes dataset, without fancy network modules, our proposed EZFusion makes remarkable improvements over the LiDAR-only baseline, and achieves comparable performance with the state-of-the-art fusion-based methods.

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.

Object tracking is an important research direction of space Earth observation in the field of remote sensing. Although the existing correlation filter-based and deep learning (DL)-based object tracking algorithms have achieved great success, they are still unsatisfactory for the problem of object occlusion. The occlusion caused by the complex change in background, and the deviation of the tracking lens, causes object information to go missing, which leads to the omission of detection. Traditionally, most methods for object tracking under occlusion adopt a complex network model, which redetects the occluded object. To address this issue, we propose a novel object tracking approach. First, an action decision-occlusion handling network (AD-OHNet) based on deep reinforcement learning (DRL) is built to achieve low computational complexity for object tracking under occlusion. Second, the temporal and spatial context, the object appearance model, and the motion vector are adopted to provide the occlusion information, which drives actions in reinforcement learning under complete occlusion and contributes to improving the accuracy of tracking while maintaining speed. Finally, the proposed AD-OHNet is evaluated on three remote sensing video datasets of Bogota, Hong Kong, and San Diego taken from Jilin-1 commercial remote sensing satellites. The video datasets all shared problems of low spatial resolution, background clutter, and small objects. Experimental results on the three video datasets validate the effectiveness and efficiency of the proposed tracker.

Publication in the conference proceedings of EUSIPCO, Antalya, Turkey, 2005