YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video

<p>The ultimate goal of this study is to afford enhanced video object detection and tracking by eliminating the limitations which are existing nowadays. Although high performance ratio for video object detection and tracking is achieved in the earlier work it takes more time for computation. Consequently we are in need to propose a novel video object detection and tracking technique so as to minimize the computational complexity. Our proposed technique covers five stages they are preprocessing, segmentation, feature extraction, background subtraction and hole filling. Originally the video clip in the database is split into frames. Then preprocessing is performed so as to get rid of noise, an adaptive median filter is used in this stage to eliminate the noise. The preprocessed image then undergoes segmentation by means of modified region growing algorithm. The segmented image is subjected to feature extraction phase so as to extract the multi features from the segmented image and the background image, the feature value thus obtained are compared so as to attain optimal value, consequently a foreground image is attained in this stage. The foreground image is then subjected to morphological operations of erosion and dilation so as to fill the holes and to get the object accurately as these foreground image contains holes and discontinuities. Thus the moving object is tracked in this stage. This method will be employed in MATLAB platform and the outcomes will be studied and compared with the existing techniques so as to reveal the performance of the novel video object detection and tracking technique. </p>

Soft Computing based object detection and tracking approaches: State-of-the-Art survey

Video object segmentation and tracking are two essential building blocks of smart surveillance systems. However, there are several issues that need to be resolved. Threshold decision is a difficult problem for video object segmentation with a multi-background model. In addition, some conditions make robust video object tracking difficult. These conditions include nonrigid object motion, target appearance variations due to changes in illumination, and background clutter. In this paper, a video object segmentation and tracking framework is proposed for smart cameras in visual surveillance networks with two major contributions. First, we propose a robust threshold decision algorithm for video object segmentation with a multi-background model. Second, we propose a video object tracking framework based on a particle filter with the likelihood function composed of diffusion distance for measuring color histogram similarity and motion clue from video object segmentation. The proposed framework can track nonrigid moving objects under drastic changes in illumination and background clutter. Experimental results show that the presented algorithms perform well for several challenging sequences, and our proposed methods are effective for the aforementioned issues.

This paper describes an automated video analysis based monitoring system with processing at the sensor edge to watch and report certain predetermined events and unusual activities in remote areas that may be in unfriendly zones. The prototype system developed here involves content extraction from video streams collected by unattended ground cameras, tracking of objects, detection of events, and assessment of scenes for anomalous situations. The application requirements impose efficiency constraints on video analysis algorithms due to low-power on the sensor processing board. We present efficient video analysis algorithms for detection, tracking and classification of objects, analysis of extracted object and scene information to detect specific events as well as anomalous or novel situations at the video camera level. Our multi-tier and modular video analysis approach uses fast a space-based peripheral vision component for quick spatially based tracking of objects, detailed object- or scene-based feature extractors and data driven Support Vector Machine (SVM) classifiers that handle feature-based analysis at multiple data levels. Our algorithms are developed and tested on PC platform but designed to match the processing and power limitations of the target hardware platform. The video object detection and tracking components have been implemented on Texas Instruments DM642 evaluation board for assessing the feasibility of the prototype system.

Satellite video single object tracking: A systematic review and an oriented object tracking benchmark

Segmenting and tracking of objects in video is of great importance for video-based encoding, surveillance, and retrieval. However, the inherent difficulty of object segmentation and tracking is to distinguish changes in the displacement of objects from disturbing effects such as noise and illumination changes. Therefore, in this paper, we formulate a color-based deformable model which is robust against noisy data and changing illumination. Computational methods are presented to measure color constant gradients. Further, a model is given to estimate the amount of sensor noise through these color constant gradients. The obtained uncertainty is subsequently used as a weighting term in the deformation process. Experiments are conducted on image sequences recorded from three-dimensional scenes. From the experimental results, it is shown that the proposed color constant deformable method successfully finds object contours robust against illumination, and noisy, but homogeneous regions.

In order to achieve facial object detection and tracking in video, a method based on nonlinear sequence Monte Carlo filtering technology is proposed. The algorithm is simple, effective, and easy to operate, which can solve the problems of scale change and occlusion in the process of online learning tracking, so as to ensure the smooth implementation of learning effect evaluation. Experimental methods should be added to the article summary section. The results show that the algorithm in this study outperforms the basic KCF in terms of evaluation accuracy and success rate, as well as outperforms other tracker algorithms in benchmark, achieving scores of 0.837 and 0.705, respectively. In terms of overlapping accuracy, the reason why this study’s algorithm is higher than KCF is that this study determines the tracking status of the current target by calculating the primary side regulated (PSR) value when the target is obscured or lost, which does not make the tracking error to accumulate. The tracking algorithm in this study is not ranked first in the two attributes of motion blur and low resolution, but the rankings of all other nine attributes belong to the first. Compared with the KCF algorithm, the accuracy plots for the three attributes of scale change, occlusion, and leaving the field of view are improved by 10.26, 13.48, and 13.04%, respectively. Thus, it is proved that the method based on nonlinear sequence Monte Carlo filtering technology can achieve video facial object detection and tracking.

Online multi-object tracking via robust collaborative model and sample selection

Multiple Object detection and tracking is a predominant area in computer vision and has numerous applications, including healthcare monitoring, autonomous driving, and anomaly detection. The proposed study is focused on multiple object detection and tracking under complex environments using advanced deep learning frameworks. Multiple object detection from videos under complex environments presents several challenges due to the dynamic and unpredictable nature of real-world conditions. The study summarizes the state-of-the-art methods, including important algorithms, datasets, and assessment measures, making it a fundamental resource for scholars and practitioners. It also highlights the shortcomings and difficulties in current solutions, offering information about areas that need more research. In conclusion, this study highlights new developments and approaches for the future, promoting the creation of innovative models and frameworks that can tackle outstanding problems in object tracking and detection.

learning-based image processing models, which are among recent data processing techniques [7].

This paper addresses the problem of identifying and tracking moving objects in a video sequence having a time-varying background. This is a fundamental task in many computer vision applications, though a very challenging one because of turbulence that causes blurring and spatiotemporal movements of the background images. Our proposed approach involves two major steps. First, a moving object detection algorithm that deals with the detection of real motions by separating the turbulence-induced motions using a two-level thresholding technique is used. In the second step, a feature-based generalized regression neural network is applied to track the detected objects throughout the frames in the video sequence. The proposed approach uses the centroid and area features of the moving objects and creates the reference regions instantly by selecting the objects within a circle. Simulation experiments are carried out on several turbulence-degraded video sequences and comparisons with an earlier method confirms that the proposed approach provides a more effective tracking of the targets.

This book chapter presents recently developed approaches for object tracking in video combining the structural similarity measure with particle filtering techniques.

Object detection and tracking in maritime videos are crucial for marine management today. Nevertheless, due to the small size of ships in maritime video, current state-of-the-art algorithms are incapable of producing satisfactory results. We design an improved YOLOX (iYOLOX) and DeepSORT-based method to detect and track small objects in maritime videos to improve detection accuracy. Initially, the primary feature extraction capability of YOLOX’s backbone network is improved by adding the convolutional block attention mechanism. Second, we employ the context enhancement module to enlarge the receptive field while preserving the specific features. Finally, a parallel deformable convolution bifusion feature pyramid network is utilized for bidirectional feature extraction for small objects. iYOLOX is additionally combined with the DeepSORT algorithm to track targets. The experimental results show iYOLOX performed better when compared with the original YOLOX such that iYOLOX improves 3% average precision (AP) on the COCO2017 dataset, 4.7% APs for small-size objects, and 3.41% AP50 on the LMD-TShip maritime video dataset. Furthermore, it indicates that the proposed algorithm effectively alleviates some object occlusion and camera shaking issues and improves the model’s utility and robustness.

Object tracking in video is a challenging problem in several applications such as video surveillance, video compression, video retrieval, and video editing. Tracking an object in a video is not easy due to loss of information caused by illumination changing in a scene, occlusions with other objects, similar target appearances, and inaccurate tracker responses. In this paper, we present a novel object detection and tracking algorithm via structured output prediction classifier. Given an initial bounding box with its position, we first divide it into sub-blocks with a predefined size. Next, we extract the features from each sub-blocks with Haar-like features method. And then we learn those features with a structured output prediction classifier. We treat the sub-blocks obtained from the initial bounding box as positive samples and then randomly choose negative samples from search windows defined by the specific area around the bounding box. After that, we obtain prediction scores for each sub-blocks both from positive and negative samples. We construct a region-graph with sub-blocks as nodes and classifier’s score as weight to detect the target object in each frame. Our experimental results show that the proposed method outperforms state-of-the-art object tracking algorithms.KeywordsObject DetectionObject TrackingSupport Vector Machine

This paper presents a correlation filter object tracker based on fast spatial-spectral features (FSSF) to realize robust, real-time object tracking in hyperspectral surveillance video. Traditional object tracking in surveillance video based only on appearance information often fails in the presence of background clutter, low resolution, and appearance changes. Hyperspectral imaging uses unique spectral properties as well as spatial information to improve tracking accuracy in such challenging environments. However, the high-dimensionality of hyperspectral images causes high computational costs and difficulties for discriminative feature extraction. In FSSF, the real-time spatial-spectral convolution (RSSC) kernel is updated in real time in the Fourier transform domain without offline training to quickly extract discriminative spatial-spectral features. The spatial-spectral features are integrated into correlation filters to complete the hyperspectral tracking. To validate the proposed scheme, we collected a hyperspectral surveillance video (HSSV) dataset consisting of 70 sequences in 25 bands. Extensive experiments confirm the advantages and the efficiency of the proposed FSSF for object tracking in hyperspectral video tracking in challenging conditions of background clutter, low resolution, and appearance changes.