Multiclass Multiple Object Tracking

We propose a real-time multiple object tracking method for smart environment applications. The proposed method combines identity features and tracking features for robust long-term object tracking. Face is a stable feature for identifying different subjects. However, face is not reliable for object tracking. Whole body color histogram is more resistant to object scale and pose changes, which has been widely applied in short-term object tracking. Unfortunately, color histograms are not stable over a relatively long period of time. In smart environments, same subjects may have different clothes over a long time span. We propose a method to combine these two features, the identity features and tracking features, to achieve reliable multiple object tracking in smart environments. A fast object labeling approach is proposed to track multiple objects in real time. The proposed method has clear advantage over traditional single feature methods. Experiments confirm that the proposed method can reliably track multiple human objects in real time through long video sequences.

Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.

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.

Real-time object detection and tracking in video streams has become a fundamental component of modern intelligent systems, enabling applications in surveillance, autonomous driving, robotics, healthcare monitoring, and smart cities. The rapid advancement of deep learning techniques has significantly improved the accuracy and efficiency of detecting and tracking multiple objects under complex and dynamic environments. This study presents a deep learning–based framework for real-time object detection and tracking in continuous video streams, emphasizing both computational efficiency and detection robustness.The proposed system integrates a state-of-the-art convolutional neural network (CNN)-based object detector with a reliable multi-object tracking algorithm. The detection module identifies objects of interest in each frame, while the tracking component maintains consistent identities across consecutive frames by utilizing motion prediction and feature association strategies. To achieve real-time performance, the model is optimized through lightweight network architectures and parallel processing on GPU hardware. Data augmentation and transfer learning techniques are employed to enhance generalization and reduce training time. Performance evaluation is conducted on benchmark video datasets using standard metrics such as precision, recall, mean Average Precision (mAP), and tracking accuracy. Experimental results demonstrate that the proposed framework achieves high detection accuracy while maintaining low latency, making it suitable for real-time deployment. Furthermore, the system effectively handles challenges such as occlusion, scale variation, and illumination changes. This research presents a comprehensive study on real-time object detection and tracking in video streams using deep learning techniques. The proposed framework integrates YOLO-based object detection with Deep SORT multi-object tracking to achieve robust spatial localization and temporal identity preservation. The study evaluates performance using benchmark datasets including COCO, MOT17, and KITTI. Mathematical formulations of detection loss, Kalman filtering, and data association are presented. Experimental analysis using precision, recall, mAP, MOTA, ROC-AUC, and PR-AUC demonstrates that the system achieves high detection accuracy while maintaining real-time performance above 35 frames per second. The findings confirm that deep learning-based frameworks provide scalable and efficient solutions for surveillance, autonomous vehicles, and intelligent monitoring systems.

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 are two critical tasks in computer vision, widely applied in security surveillance, autonomous vehicles, and behavioral analysis. Strong performance in object detection has been demonstrated by recent Transformer-based models, such as RT-DETR (Real-Time Detection Transformer), due to their global context modeling and high accuracy. However, an inherent tracking mechanism is lacking in RT-DETR, which requires additional components to maintain identity consistency across frames. To address this limitation, an integration of RT-DETR with DeepSORT is proposed, leveraging the strengths of both models to enhance real-time object detection and tracking. A comparative evaluation with YOLOv8, a widely used real-time detector, is conducted to highlight the advantages of the proposed approach in tracking accuracy and robustness. Experiments show that effective performance is achieved in challenging scenarios such as object occlusion and intersection. Specifically, an IDF1 score of 60.0%, a MOTA of 42.4%, and a MOTP of 43.3% are obtained by RTl+DeepSORT on the MOT17-02-DPM dataset, outperforming YOLOv8x+DeepSORT. These results indicate that significant improvements in tracking accuracy are attained while real-time efficiency is maintained, making the proposed approach well-suited for applications such as intelligent surveillance and autonomous navigation.

The ability of Convolutional Neural Networks (CNNs) to learn from vast amounts of data and improve accuracy over time makes them an attractive solution for many industrial problems. In the context of Future Assembly Systems such as Line-Less Mobile Assembly Systems, CNNs can be used to monitor the networked system of mobile robots, human operators, and other movable objects that assemble products in flexible environment configurations. This paper explores the use of a simulated industrial environment to autonomously generate training data for object detection, tracking, and segmentation CNNs. The goal is to adapt state-of-the-art CNN solutions to specific industry use cases, where real data annotation can be time-consuming and expensive. The developed algorithm efficiently generates new random image data, allowing accurate object detection, tracking, and segmentation in dynamic industrial scenarios. The results show the effectiveness of this approach in improving the testing of CNNs for industrial applications.

Artificial vision in robotics involves real time detection of objects for fast decision making. Such intelligent systems require efficient algorithms and big learning database of examples for producing robust classifiers. Several methods of objects detection and tracking have been proposed in the literature. However, even though the detection rates have been improved, the processing time and the complexity of the models still representing a key challenge. In this paper, we present a real time object detection and tracking framework based on Adaboost classification, where a strong classifier is generated using an iterative combination of weak learners. This method is based on the use of discriminative features by analyzing different regions of the input image. Instead of performing a full traversal in the entire search space of all possible visual features, we propose to use intelligent heuristics for accelerating time processing and extracting relevant features in the image that lead to a best detection rate. The meta-heuristics involve the use of genetic algorithms, particle swarm optimization, random walk and a novel hybrid combination of these methods. The obtained results, in a case of intelligent transportation system, have shown considerable improvements in term of computation time, efficiency and accuracy.

This research proposes a novel hardware-optimized design to accelerate Convolutional Neural Networks (CNNs) using Verilog HDL. The design is specifically developed for the DARKNET-19 system model, which serves as the backbone of the YOLOv3-tiny algorithm, a widely used framework for real-time object detection in dynamic environments. The CNN architecture was implemented in Verilog HDL and synthesized using Synopsys Design Compiler, with a focus on improving both object detection accuracy and hardware resource efficiency. The proposed design efficiently performs key CNN operations, including convolution, pooling, and activation, enabling faster real-time object detection compared to many existing methods. To improve performance, the hardware design incorporates parallel processing techniques, allowing multiple computations to be executed simultaneously. This significantly reduces the system latency and power consumption. The convolutional layers of the DARKNET-19 architecture are efficiently mapped onto the hardware platform, ensuring optimized data storage and fast memory access, which further enhances processing speed and detection accuracy. An innovative feature of the design is a 2-dimensional image preprocessing module that prepares input images before they are fed into the CNN. This preprocessing stage includes image resizing, brightness normalization, and color adjustment, which helps the CNN process visual data more effectively. After preprocessing, the images pass through several CNN layers. The convolutional layers extract key features from the images, while the pooling and activation layers refine these features to improve detection performance. Finally, the processed data is analyzed by the YOLOv3-tiny algorithm, which identifies and locates objects in the images with high precision. Experimental results demonstrate that the proposed high-speed and resource-efficient hardware architecture is well-suited for real-time object detection applications, particularly in highly dynamic and unpredictable environments.

Real-time detection and tracking for fast moving object has important applications in various fields. However, available methods, especially low-cost ones, can hardly achieve real-time and long-duration object detection and tracking. Here we report an image-free and cost-effective method for detecting and tracking a fast moving object in real time and for long duration. The method employs a spatial light modulator and a single-pixel detector for data acquisition. It uses Fourier basis patterns to illuminate the target moving object and collects the resulting light signal with a single-pixel detector. The proposed method is able to detect and track the object with the single-pixel measurements directly without image reconstruction. The detection and tracking algorithm of the proposed method is computationally efficient. We experimentally demonstrate that the method can achieve a temporal resolution of 1,666 frames per second by using a 10,000 Hz digital micro-mirror device. The latency time of the method is on the order of microseconds. Additionally, the method acquires only 600 bytes of data for each frame. The method therefore allows fast moving object detection and tracking in real time and for long duration. This image-free approach might open up a new avenue for spatial information acquisition in a highly efficient manner.

Low complexity object detection and tracking with inter-layer graph mapping and intra-layer graph refinement in H.264/SVC bitstreams

In the tracking‐by‐detection approach of online multiple object tracking (MOT), a major challenge is how to associate object detections on the new video frame with previously tracked objects. Two important aspects that directly influence the performance of MOT are quality of detection and accuracy in data association. The authors propose an efficient and unified MOT framework for improved object detection, followed by enhanced object tracking. The object detection and tracking are considered as two independent functions in the tracking‐by‐detection paradigm. In this study, object detection accuracy has been increased by employing a faster region‐based convolutional neural network (Faster R‐CNN) modified with the feedback region proposals from the tracker. Target association is performed by the correlation filter‐based Siamese CNN model, which finds the similarity score between the input image patches. The Siamese CNN is trained using a supervised hard sample mining strategy. An optical flow‐based motion model is employed to predict the next probable location of the targets from the tracker and these region proposals are fed back to the classifier module of Faster R‐CNN. The authors’ extensive analysis of publicly available MOT benchmark datasets and comparison with the state‐of‐the‐art tracking methods demonstrate competitive tracking performance of the proposed MOT framework.

There is a real need to develop a sensory system that can monitor and track the behavioral status of a person in real time while protecting his/her privacy. In addition, it is important to have the ability to detect stationary and moving objects in real time to support dynamic navigation needs for mobile robots. This paper describes the development of intelligent fiber-grating (FG)-based 3-D vision sensory system that enables real-time object detection, monitoring, and tracking. This system uses the richness and the strength of the vision while reducing the data load and computational cost by encoding the working space using a limited number of spatially interrelated 2-D laser spots. The sensor system generates the laser spots by projecting a coherent laser beam on two perpendicularly overlaid layers of FGs. The presence of a target within the visible operational view of the sensor disturbs the part of the projected laser spots' pattern where the target appeared. By calculating the displacement of the disturbed spots within the CCD image plane, the sensor system can detect, in a short time, an object/human and provide relevant information in terms of range, approximate shape, and position. To facilitate faster detection and real-time tracking, the author has developed two strategies that decompose the working space into zones and virtual planes

Real time and accrual object tracking is ultimate aim of a visual tracker. Recently mean shift object tracking algorithm is studied in an all-round way and utilized to visual tracking in both civil and military fields. Compared to the traditional mean shift algorithm and its other versions distance weight mean shift algorithm can effectively improve the precision and robustness. It analyzes the dissimilarity of target feature color histogram with distance weight and candidate area feature color histogram with distance weight to calculate mean shift vector, and searches for target area via each iteration in candidate area. Though improved on basis of traditional mean shift algorithm, there are a large amount of calculations and the processing speed has already been a bottleneck in real time object tracking. Graphics processor unit (GPU) has special hardware parallel computing resources and has been already employed in various embedded systems. It provides us with an alternative implementation on GPU which can solve such bottleneck problem. The paper proposes a way of implementation of distance weight mean shift algorithm on GPU. The experiment results show that the execution performance of the implementation on GPU is over 56 times speedup of that completely on CPU at most, and can meet the requirement of real time and accuracy.

In computer vision application object tracking is a challenging problem. Illumination and occlusion are major constraints observed in object tracking. We are focusing on object tracking with partial or full occlusion. Object tracking is done using features like colors and contours. We have proposed a robust Color-based algorithm to track the object and handle occlusion in real time domain. In our algorithm, HSV color model is used and HSV range of the color object to be tracked is selected. Required object area is identified and contour is formed accordingly to track the object. Proposed algorithm is implemented on ARM Cortex-A7 using Open Source Computer Vision (OpenCV) and Linux-embedded platform. The experiment is done using generated real life database and standard unusual crowd activity database. Results showed run time performance, efficiency and accuracy in real time environment.