63,785 research outputs found

    Algorithms and evaluation for object detection and tracking in computer vision

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    Vision-based object detection and tracking, especially for video surveillance applications, is studied from algorithms to performance evaluation. This dissertation is composed of four topics: (1) Background Modeling and Detection, (2) Performance Evaluation of Sensitive Target Detection, (3) Multi-view Multi-target Multi-Hypothesis Segmentation and Tracking of People, and (4) A Fine-Structure Image/Video Quality Measure. First, we present a real-time algorithm for foreground-background segmentation. It allows us to capture structural background variation due to periodic-like motion over a long period of time under limited memory. Our codebook-based representation is efficient in memory and speed compared with other background modeling techniques. Our method can handle scenes containing moving backgrounds or illumination variations, and it achieves robust detection for different types of videos. In addition to the basic algorithm, three features improving the algorithm are presented - Automatic Parameter Estimation, Layered Modeling/Detection and Adaptive Codebook Updating. Second, we introduce a performance evaluation methodology called Perturbation Detection Rate (PDR) analysis for measuring performance of foreground-background segmentation. It does not require foreground targets or knowledge of foreground distributions. It measures the sensitivity of a background subtraction algorithm in detecting possible low contrast targets against the background as a function of contrast. We compare four background subtraction algorithms using the methodology. Third, a multi-view multi-hypothesis approach to segmenting and tracking multiple persons on a ground plane is proposed. The tracking state space is the set of ground points of the people being tracked. During tracking, several iterations of segmentation are performed using information from human appearance models and ground plane homography. Two innovations are made in this chapter - (1) To more precisely locate the ground location of a person, all center vertical axes of the person across views are mapped to the top-view plane to find the intersection point. (2) To tackle the explosive state space due to multiple targets and views, iterative segmentation-searching is incorporated into a particle filtering framework. By searching for people's ground point locations from segmentations, a set of a few good particles can be identified, resulting in low computational cost. In addition, even if all the particles are away from the true ground point, some of them move towards the true one through the iterated process as long as they are located nearby. Finally, an objective no-reference measure is presented to assess fine-structure image/video quality. The proposed measure using local statistics reflects image degradation well in terms of noise and blur

    Object Detection and Tracking using Modified Diamond Search Block Matching Motion Estimation Algorithm

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    Object tracking is one of the main fields within computer vision. Amongst various methods/ approaches for object detection and tracking, the background subtraction approach makes the detection of object easier. To the detected object, apply the proposed block matching algorithm for generating the motion vectors. The existing diamond search (DS) and cross diamond search algorithms (CDS) are studied and experiments are carried out on various standard video data sets and user defined data sets. Based on the study and analysis of these two existing algorithms a modified diamond search pattern (MDS) algorithm is proposed using small diamond shape search pattern in initial step and large diamond shape (LDS) in further steps for motion estimation. The initial search pattern consists of five points in small diamond shape pattern and gradually grows into a large diamond shape pattern, based on the point with minimum cost function. The algorithm ends with the small shape pattern at last. The proposed MDS algorithm finds the smaller motion vectors and fewer searching points than the existing DS and CDS algorithms. Further, object detection is carried out by using background subtraction approach and finally, MDS motion estimation algorithm is used for tracking the object in color video sequences. The experiments are carried out by using different video data sets containing a single object. The results are evaluated and compared by using the evaluation parameters like average searching points per frame and average computational time per frame. The experimental results show that the MDS performs better than DS and CDS on average search point and average computation time

    Generative Adversarial Networks for Online Visual Object Tracking Systems

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    Object Tracking is one of the essential tasks in computer vision domain as it has numerous applications in various fields, such as human-computer interaction, video surveillance, augmented reality, and robotics. Object Tracking refers to the process of detecting and locating the target object in a series of frames in a video. The state-of-the-art for tracking-by-detection framework is typically made up of two steps to track the target object. The first step is drawing multiple samples near the target region of the previous frame. The second step is classifying each sample as either the target object or the background. Visual object tracking remains one of the most challenging task due to variations in visual data such as target occlusion, background clutter, illumination changes, scale changes, as well as challenges stem from the tracking problem including fast motion, out of view, motion blur, deformation, and in and out planar rotation. These challenges continue to be tackled by researchers as they investigate more effective algorithms that are able to track any object under various changing conditions. To keep the research community motivated, there are several annual tracker benchmarking competitions organized to consolidate performance measures and evaluation protocols in different tracking subfields such as Visual Object Tracking VOT challenges and The Multiple Object Tracking MOT Challenges [1, 2]. Despite the excellent performance achieved with deep learning, modern deep tracking methods are still limited in several aspects. The variety of appearance changes over time remains a problem for deep trackers, owing to spatial overlap between positive samples. Furthermore, existing methods require high computational load and suffer from slow running speed. Recently, Generative Adversarial Networks (GANs) have shown excellent results in solving a variety of computer vision problems, making them attractive in investigating their potential use in achieving better results in other computer vision applications, namely, visual object tracking. In this thesis, we explore the impact of using Residual Network ResNet as an alternative feature extractor to Visual Geometry Group VGG which is commonly used in literature. Furthermore, we attempt to address the limitations of object tracking by exploiting the ongoing advancement in Generative Adversarial Networks. We describe a generative adversarial network intended to improve the trackerā€™s classifier during the online training phase. The network generates adaptive masks to augment the positive samples detected by the convolutional layer of the trackerā€™s model in order to improve the modelā€™s classifier by making the samples more difficult. Then we integrate this network with Multi-Domain Convolutional Neural Network (MDNet) tracker and present the results. Furthermore, we introduce a novel tracker, MDResNet, by substituting the convolutional layers of MDNet that were originally taken from Visual Geometry Group (VGG-M) network with layers taken from Residual Deep Network (ResNet-50) and the results are compared. We also introduce a new tracker, Region of Interest with Adversarial Learning (ROIAL), by integrating the generative adversarial network with the Real-Time Multi-Domain Convolutional Network (RT-MDNet) tracker. We also integrate the GAN network with MDResNet and MDNet and compare the results with ROIAL

    UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking

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    In recent years, numerous effective multi-object tracking (MOT) methods are developed because of the wide range of applications. Existing performance evaluations of MOT methods usually separate the object tracking step from the object detection step by using the same fixed object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140,000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and object tracking methods. Our analysis shows the complex effects of object detection accuracy on MOT system performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and object tracking for comprehensive analysis.Comment: 18 pages, 11 figures, accepted by CVI
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