Tracking In Video Sequences Research Articles

Multi-Object Detection and Tracking with Modified Optimization Classification in Video Sequences

The paper presents a novel approach to enhancing multi-object detection and tracking in video sequences using a Modified Ant Swarm Optimization Deep Learning (ASO-DL) algorithm. The ASO-DL algorithm synergistically combines the optimization capabilities of ant swarm optimization with the powerful feature extraction abilities of deep learning models, resulting in a robust framework for realtime video analytics. Extensive simulations and experiments demonstrate significant improvements in key performance metrics, including accuracy, precision, recall, and F1 score, across various iterations. The proposed method consistently outperforms baseline models, achieving a final best fitness value of 0.96, with an accuracy of 0.98, precision of 0.99, and recall of 0.95. Additionally, classification results across different datasets such as CIFAR-10, IMDB, COCO, and ImageNet highlight the algorithm’s versatility and effectiveness. This research contributes to the field by providing a highly optimized solution for complex multi-object tracking tasks, offering substantial advancements in the accuracy and efficiency of real-time object detection systems. The findings hold significant potential for applications in surveillance, autonomous vehicles, and other domains requiring precise and reliable multi-object tracking.

The method of the real-time human detection and tracking

Today, data collected from video surveillance systems require processing. Video content analysis (VCA) or video analytics (VA) has found applications in security systems, retail, the automotive industry, smart home technologies, etc. The tasks of video analytics include the detection and tracking of objects. Usually, video analytics systems are specialized software for analyzing video data collected from webcams and intelligently assessing the situation. There are also separate video surveillance cameras with built-in video analytics functions. Software approaches to human detection and tracking are quite diverse, special applications and web applications or separate software modules are created. This work presents an approach to solving these problems using convolutional neural networks. The aim of the study is to increase the efficiency of human detection and tracking in video sequences. For this purpose, an overview of existing methods of detecting objects in images was conducted, in particular: the Viola-Jones algorithm, the histogram of oriented gradients. The choice of convolutional neural networks for solving the subtask of object detection is substantiated. The working principles, advantages and disadvantages of Faster R-CNN, YOLO, SSD and RetinaNet networks are considered. Their comparative analysis was carried out according to the indicators of speed and accuracy of recognition on the HABBOF dataset. A hybrid neural network for human detection and tracking has been developed: a convolutional neural network of the YOLO type has been improved. The created network achieved an accuracy of 39.2% at 43 frames per second. Experiments were carried out with the created network in order to evaluate the operation of the network in various conditions. It has been established that it works well in poor lighting conditions, but the issue of detecting small objects remains open.

Improved Adaboost-Camshift Face Tracking System in Complex Background

With the rapid growth of science and technology, people pay more and more attention to pattern recognition and computer interaction. Therefore, in the last few years, face detection and tracking technology in video sequence has become a hot topic for people to study. Face tracking detection has a wide application prospect in human-computer interaction, intelligent monitoring, video conference and other aspects. In this paper, the problem of face tracking in video sequences is divided into two aspects: face detection and moving object tracking algorithm. In the face detection problem, the face detection based on Adaboost algorithm is described in detail, and the three-frame difference method is added to make the algorithm better and enhance the speed of face detection. In terms of moving object tracking algorithm, Camshift face tracking algorithm based on color histogram is adopted, which is not affected by the shape and size of the target and has good real-time performance. However, under the influence of color interference and occlusion, the algorithm will make tracking errors. Therefore, Kalman filter is introduced. The algorithm can directly delineate the candidate areas of face to be detected, so as to ensure the feasibility of face tracking. The simulation video image face tracking system is verified by Matlab software. The experimental results show that the system can accurately detect and track the faces in the video image sequence, not only in the simple background, but also in the complex background and multiple faces can also be well detected and tracked, and the tracking ensures real-time performance.

Open-Space Vehicle Positioning Algorithm Based on Detection and Tracking in Video Sequences.

In on-street parking lots, it is very important to obtain the positions and license plate numbers of the vehicles for charging and management. Existing algorithms usually detect vehicles from an input image first, then localize their license plates, and finally recognize the license plate numbers. However, they are of high time and space complexity and cannot work if the vehicles or license plates are obscured. Therefore, this paper proposes an open-space vehicle positioning algorithm based on detection and tracking in video sequences. The work is as follows: (1) To reduce the time and space complexity, parallel detection of vehicles and license plates is carried out. Then, geometric matching is performed to accomplish the correspondences between them. (2) To track vehicles and license plates, this paper improves DeepSORT by combining with integrated voting based on the historical license plate library. (3) To accurately judge the vehicle behavior of entry or exit, a cumulative state detector is designed to increase the fault-tolerance of the proposed algorithm. The experimental results reveal that the proposed algorithm makes improvements in model parameters, inference speed, and tracking accuracy, demonstrating that it can be well applied to open-space vehicle positioning.

Siamese network based on global and local feature matching for object tracking

Some tracking algorithms based on Siamese network have made great progress in similarity learning via features cross-correlation between an object branch and a search branch. However, it is significantly challenging for object tracking in video sequences in terms of target deformation with greatly varying. We propose a Siamese network based on global and local feature matching for object tracking including three phases with the aim of addressing the above issues. In the first phase, obtaining the global similarity matching and local relational mapping similarity of the template branch and the search branch by a selection mechanism of object template-aware features are to reduce the impact of background features on the local matching. In the second phase, introducing correlation matching of the local feature for establishing correspondence among partial-level pixels. Finally, combining the classification and regression results with global matching features and local matching features in a weighted fusion. Extensive experiments are conducted on datasets (OTB-100, LaSOT and GOT-10K) demonstrate that the proposed network enables to achieve superiority compared against the state-of-the-art method and provides an efficient scenario for tackling the issue.

Kalman Filtering and Bipartite Matching Based Super-Chained Tracker Model for Online Multi Object Tracking in Video Sequences

Object tracking has gained importance in various applications especially in traffic monitoring, surveillance and security, people tracking, etc. Previous methods of multiobject tracking (MOT) carry out detections and perform object tracking. Although not optimal, these frameworks perform the detection and association of objects with feature extraction separately. In this article, we have proposed a Super Chained Tracker (SCT) model, which is convenient and online and provides better results when compared with existing MOT methods. The proposed model comprises subtasks, object detection, feature manipulation, and using representation learning into one end-to-end solution. It takes adjacent frames as input, converting each frame into bounding boxes’ pairs and chaining them up with Intersection over Union (IoU), Kalman filtering, and bipartite matching. Attention is made by object attention, which is in paired box regression branch, caused by the module of object detection, and a module of ID verification creates identity attention. The detections from these branches are linked together by IoU matching, Kalman filtering, and bipartite matching. This makes our SCT speedy, simple, and effective enough to achieve a Multiobject Tracking Accuracy (MOTA) of 68.4% and Identity F1 (IDF1) of 64.3% on the MOT16 dataset. We have studied existing tracking techniques and analyzed their performance in this work. We have achieved more qualitative and quantitative tracking results than other existing techniques with relatively improved margins.

An Efficient Unusual Event Tracking in Video Sequence using Block Shift Feature Algorithm

The area of video technology is rapidly growing owing to advancements in intelligent video systems in sensor operations, higher bandwidth capacity, storage, and high-resolution displays. This led to the proliferation of video-based computing modeling to perform specific tasks on video sequences to gain more insight from the data. Visual tracking of events is a core component in video visual surveillance systems that classify and track moving objects to describe their behavioral aspects. The prime motive behind intelligent video systems is to perform efficient video analytics to meet the specific requirements of the user/use-cases. It involves a self-directed paradigm to understand event sequences, reducing the computational burden of characterizing the activities. The study incorporates a block-shift feature algorithm and introduces a novel computational research method for unusual event tracking in video sequences. The formulated approach employs a framework combining operational blocks to compute sequential operations such as block-matching from the dictionary of motion estimations. Before applying the learning model, the subsequent analysis procedure adds feature lexicon and dominant attributes to make the execution computationally efficient. Further, it uses a sparse-non negative factorization approach to organize the informative details into k possible finite clusters. The event detection outcome from the training datasets of video sequences shows better experimental results than the traditional highly cited related approach of unusual object detection and tracking.

Body-Part Enabled Wildlife Detection and Tracking in Video Sequences

Body-Part Enabled Wildlife Detection and Tracking in Video Sequences

Automatic Recognition System of Motion Posture Based on Information Video Processing Technology

With the rapid development of science and technology, information video processing technology has become a research field that has attracted widespread attention. Automatic recognition of motion gestures is an important research object in the field of information technology. It has been widely used in education, medical treatment and finance, and has promoted the further development of this technology. The application of information video processing technology in motion posture and automatic recognition of motion posture contribute to better scientific education and technical diagnosis. This article is based on the research of automatic motion detection system based on video information processing technology. Based on the summary and analysis of relevant research work at home and abroad, this paper mainly studies the detection of moving posture targets, the segmentation of moving posture targets, and the automatic recognition of moving posture tracking in video sequences based on the characteristics of the motion posture. This research aims to effectively improve the effect of automatic recognition of sports high poses while appropriately reducing the computational complexity. The research focuses on finding simpler and more informative automatic recognition methods of sports poses, and a large number of targeted methods have been carried out. This research uses these algorithms to extract moving images from videos, and proposes an automatic recognition method of motion gestures based on information video processing technology. Experimental results show that the average error rate of the system designed in this paper is 89.5%, which can meet the current application requirements for automatic recognition of motion gestures.

Robust model adaption for colour-based particle filter tracking with contextual information

Color-based particle filters have emerged as an appealing method for targets tracking. As the target may undergo rapid and significant appearance changes, the template (i.e. scale of the target, color distribution histogram) also needs to be updated. Traditional updates without learning contextual information may imply a high risk of distorting the model and losing the target. In this paper, a new algorithm utilizing the environmental information to update both the scale of the tracker and the reference appearance model for the purpose of object tracking in video sequences has been put forward. The proposal makes use of the well-established color-based particle filter tracking while differentiating the foreground and background particles according to their matching score. A roaming phenomenon that yields the estimation to shrink and diverge is investigated. The proposed solution is tested using both simulated and publicly available benchmark datasets where a comparison with six state-of-the-art trackers has been carried out. The results demonstrate the feasibility of the proposal and lie down foundations for further research on tackling complex visual tracking problems.

Adaptive fuzzy weighted color histogram and HOG appearance model for object tracking with a dynamic trained neural network prediction

Object tracking in video sequences represents an important task in computer vision applications like human mobility and social distance in pandemic scenarios, intelligent surveillance systems, etc. This paper presents a new method, fuzzy weighted color histogram neural network (FWCHNN) to estimate the position of an object and to update the appearance object model during tracking. The method involves two stages. Prediction is achieved through an online dynamic trained neural network, and correction is performed with a spiral sliding window. The method employs a new similitude appearance measure based on a codified model of the object considering its shape and color. The shape is represented with HOG features and the color features from information of a fuzzy weighted color histogram. FWCHNN shows the comparable results regarding state-of-the-art methods.

Object detection and tracking in video sequences: formalization, metrics and results

One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the detection and tracking of one and many objects in video. The following metrics are considered: the quality of detection of tracked objects, the accuracy of determining the location of the object in a frame, the trajectory of movement, the accuracy of tracking multiple objects. Based on the considered generalization, an algorithm for tracking people has been developed that uses the tracking through detection method and convolutional neural networks to detect people and form features. Neural network features are included in a composite descriptor that also contains geometric and color features to describe each detected person in the frame. The results of experiments based on the considered criteria are presented, and it is experimentally confirmed that the improvement of the detector operation makes it possible to increase the accuracy of tracking objects. Examples of frames of processed video sequences with visualization of human movement trajectories are presented.

Efficient face detection and tracking in video sequences based on deep learning

Video-based face detection and tracking technology has been widely used in video surveillance, safe driving, and medical diagnosis. In video sequences, most existing face detection and tracking methods face interference caused by occlusion, ambient illumination, and changes in human posture. To accurately track human faces in video sequences, we propose an efficient face detection and tracking framework based on deep learning, which includes a SENResNet face detection model and a Regression Network-based Face Tracking (RNFT) model. Firstly, the SENResNet model integrates the Squeeze and Excitation Network (SEN) with the Residual Neural Network (ResNet). To solve the problem that deep neural networks are difficult to train, we use ResNet to overcome the problem of gradient disappearance in deep network training. To fuse the features of each channel during the convolution operation, we further integrate the SEN module into the SENResNet model. SENResNet accurately detects facial information in each frame and extracts the position of the target face, thereby providing an initialization window for face tracking. Then, the RNFT model extracts facial features from adjacent frames and predict the position of the target face in the next frame. To address the problem of feature scaling, we add a correction network to the RNFT model. The improved RNFT model extracts the rectangular frame of the target face in the previous frame and strengthens the perception of feature scaling, thereby improving its accuracy. Extensive experimental results on public facial and video datasets show that the proposed SENResNet and RNFT models are superior to the state-of-the-art comparison methods in terms of accuracy and performance.

Object Detection and Tracking in Real Time Video

Object tracking is the process of locating moving objects over time using the camera in video sequences. The objective of object tracking is to associate target objects in consecutive video frames. Object tracking requires the location and shape or features of objects in the video frames. So, object detection and object classification are the preceding steps of object tracking in computer vision applications. To detect or locate the moving object in the frame, Object detection is the first stage in tracking. It is a challenging or difficult task in image processing to track the objects into consecutive frames. Various challenges can arise due to complex object motion, irregular shape of object, occlusion of object to object and object to the scene, and real-time processing requirements. This paper presents the various techniques of object tracking in video sequences.

Vision-Based Moving UAV Tracking by Another UAV on Low-Cost Hardware and a New Ground Control Station

Automatic flying target detection and tracking in video sequences acquired from a camera mounted on another Unmanned Aerial Vehicle (UAV) is a challenging task due to the presence of non-stationary cameras in the system, dynamic motion of the moving target, and high-cost computation for real-time applications. In this paper, our aim is to automatically detect and track moving UAV by another one while simultaneously flying in the air. In order to provide efficiently in real-time applications, we develop a vision-based low-cost hardware system integrated with an independent ground control station. We initially created a new public dataset called ATAUAV that includes different types of UAV images obtained from videos recording in our experiments and searches on Google Images for the training process. Deep learning-based YOLOv3-Tiny (You Only Look Once) is used for target detection with the highest accuracy and fastest results. Kernelized Correlation Filter (KCF) adapted with YOLO, which runs on low-cost hardware, is used for real-time detected target tracking. We compared the performance of the proposed approach with different tracking algorithms. Experimental results show that the proposed approach provides the highest accuracy rate as 82.7% and a mean fps speed as 29.6 on CPU. The dataset can be downloaded at http://cogvi.atauni.edu.tr/ResearchLab/PageDetail/Our-ATAUAVs-Dataset-86.

Deep Reinforcement Learning for Action Based Object Tracking in Video Sequences

Deep Reinforcement Learning for Action Based Object Tracking in Video Sequences

Visual tracking in video sequences based on biologically inspired mechanisms

Visual tracking is the process of locating one or more objects based on their appearance. The high variation in the conditions and states of a moving object and presence of challenges such as background clutter, illumination variation, occlusion, etc. makes this problem extremely complex, and hard to achieve a robust algorithm in this field. However, unlike the machine vision, in the biological vision, the task of visual tracking is ideally conducted even in the worst conditions. Consequently, in this paper, taking into account the superior performance of biological vision in visual tracking, a biologically inspired visual tracking algorithm is introduced. The proposed algorithm inspiring the task-driven recognition procedure of the primary layers of the ventral pathway, and visual cortex mechanisms including spatial–temporal processing, motion perception, attention, and saliency to track a single object in the video sequence. For this purpose, a set of low-level features including the oriented-edges, color, and motion information (inspired by the layer V1) extracted from the target area and based on the discrimination rate that each feature creates with the background (inspired by the saliency mechanism), a subset of these features are employed to generate the appearance model and identify the target location. Moreover, by memorizing the shape and motion information (inspired by the short-term memory) scale variation and occlusion are handled. The experimental results showed that the proposed algorithm can well handle most of the visual tracking challenges, achieve high precision in target locating and act in a real-time manner.

Direct pose estimation for planar objects

Estimating six degrees of freedom poses of a planar object from images is an important problem with numerous applications ranging from robotics to augmented reality. While the state-of-the-art Perspective-n-Point algorithms perform well in pose estimation, the success hinges on whether feature points can be extracted and matched correctly on target objects with rich texture. In this work, we propose a two-step robust direct method for six-dimensional pose estimation that performs accurately on both textured and textureless planar target objects. First, the pose of a planar target object with respect to a calibrated camera is approximately estimated by posing it as a template matching problem. Second, each object pose is refined and disambiguated using a dense alignment scheme. Extensive experiments on both synthetic and real datasets demonstrate that the proposed direct pose estimation algorithm performs favorably against state-of-the-art feature-based approaches in terms of robustness and accuracy under varying conditions. Furthermore, we show that the proposed dense alignment scheme can also be used for accurate pose tracking in video sequences.

Template matching approach for automatic human body tracking in video

In this paper a novel template matching approach is presented to achieve automatic human body tracking in video sequences. The developed method which is based on a special template matching algorithm applied on a set of interest points detected on the human body contour. The matching approach is based on different types of similarity measures applied on consecutive frames from videos. Each frame was attacked with different types of noise: luminosity variation and motion blur. This new approach considers different matching constraints such as: cross-matching, uniqueness constraint and interest point's appearances and disappearances between consecutive frames. The algorithm was validated on two different datasets and the obtained results are so encouraging with high values of matching rate and good tracking rate.

The Research on Object Detection and Tracking in Video Sequence

This paper investigates object detection and tracking in a video sequence. The detection and tracking technologies have been extensively applied in numerous domains. The object is defined by its location and extent in a single frame. In every frame that follows, the task is to confirm the object’s location and extent or indicate that the object is not present. Since the performance varies from the object background in the video, such as the occlusion, lighting and the variation scale, it is essential to improve algorithms for detecting and tracking the object in video sequence. This paper provides a detailed introduce to the traditional and the state-of-the-art approaches, analysis the advantages and disadvantages of these methods. After summarizing previous research works, we indicate some problems that should be studied and solved in the future.