Research on Multitarget Detection and Intelligent Tracking Technology Based on Computer Vision

To realize the multi-targets detection, tracking and identification by the dissimilar sensors fusion in the air early-warning and surveillance system, the multi-targets joint detection, tracking and recognition strategy based on the random finite set model for the dissimilar sensors fusion is studied. Through the unified description of the target's kinematics state and target recognition attribute state, the multi-targets state is modeled as a global state described by a random finite set. By analyzing the kinematic sensor and attribute sensor models, the various sensors are modeled as a global sensor, and then each dissimilar sensor measurement is modeled as a global measurement described by a random finite set. According to the global state and the global measurement model, the process of multi-targets joint detection, tracking and recognition by dissimilar sensors fusion is described as a Bayesian filtering process, and the corresponding strategy of multi-targets joint detection, tracking and recognition has been proposed. Simulated experiment is given to prove the correctness and effectiveness of the proposed strategy.

Rubber Tire Gantry (RTG) plays a pivotal role in facilitating efficient container handling within port operations. Conventional RTG, highly depending on human operations, is inefficient, labor-intensive and also poses safety issues in adverse environments. This paper introduces a multi-target detection and tracking (MTDT) algorithm specifically tailored for automated port RTG operations. The approach seamlessly integrates enhanced YOLOX for object detection and improved DeepSORT for object tracking to enhance MTDT performance in the complex port settings. In particular, Light-YoloX, an upgraded version of YOLOX incorporating separable convolution and attention mechanism, is introduced to improve real-time capability and small target detection. Subsequently, OSNet-DeepSORT, an enhanced version of DeepSORT, is proposed to mitigate ID switching challenges arising from unreliable data communication or occlusion in real port scenarios. The effectiveness of the proposed method is validated in various real-life port operations. Ablation studies and comparative experiments against typical MTDT algorithms demonstrate noteworthy enhancements in key performance metrics, encompassing small target detection, tracking accuracy, ID switching frequency, and real-time performance.

There has been much research on computer vision in last three decades. Computer vision methods have been developed for different situations. One example is a detection of human face. For computers face detection is hard. Faces look different from different viewing directions. Facial expressions affect to the look of the face. Each individual person has a unique face. The lightning conditions can vary and so on.However, face detection is currently possible in limited conditions. In addition, there are some methods that can be used for gender recognition [3], face recognition [5] and facial expression recognition [2].Nonetheless, there has been very little research on how to combine these methods. There has also been quite little research on how to apply these methods in human-computer interaction (HCI).Finding sets of techniques that complement each other in a useful way is one research challenge. There are some applications that take advantage of one or two computer vision techniques. For example, Christian and Avery [1] have developed an information kiosk that uses computer vision to detect potential users from a distance. A similar kiosk has been developed by us in the University of Tampere [4]. There are also some games that use simple computer vision techniques for the interaction. However, there are very few applications that use several computer vision techniques together such as face detection, facial expression recognition and gender recognition. Overall, there has been very little effort in combining different techniques.In my research I develop computer vision methods and combine them, so that the combined method can detect face, recognize gender and facial expressions. After successfully combining the methods, it is easier to develop HCI applications that take advantage of computer vision. Applications that can be used by small group of people are my specific interest. These applications allow me to build adaptive user interfaces and analyze the use of computer vision techniques in improving human-computer interaction.

A dynamic programming (DP) multi-target detection and tracking algorithm is developed for the simultaneous detection and tracking of low signal - noise ratio (SNR) targets in passive sonar processing. The detecting and tracking approach is considered as a special problem of multiple dimensional image processing and integrated into a single optimization procedure. The DP algorithm is extended to passive sonar processing and a new trace-evaluation function is proposed in this paper. Numerical simulation result shows the advantage of DP algorithm.

Evaluation of Surface Finish Quality Using Computer Vision Techniques

Face recognition technology automatically identifies an individual from image or video sources. The detection process can be done by attaining facial characteristics from the image of a subject face. Recent developments in deep learning (DL) and computer vision (CV) techniques enable the design of automated face recognition and tracking methods. This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking (HHODL-AFDT) method. The proposed HHODL-AFDT model involves a Faster region based convolution neural network (RCNN)-based face detection model and HHO-based hyperparameter optimization process. The presented optimal Faster RCNN model precisely recognizes the face and is passed into the face-tracking model using a regression network (REGN). The face tracking using the REGN model uses the features from neighboring frames and foresees the location of the target face in succeeding frames. The application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the work. The experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60% and 88.08% under PICS and VTB datasets, respectively.

Advances and applications of computer vision techniques in vehicle trajectory generation and surrogate traffic safety indicators

In this paper, we present a simulation-based method for multitarget tracking and detection using sequential Monte Carlo (SMC), or particle filtering (PF) methods. The proposed approach is applicable to nonlinear and non-Gaussian models for the target dynamics and measurement likelihood, where the environment is characterised by high clutter rate and low detection probability. The number of targets is estimated by continuously monitoring the events being represented by the regions of interest (ROIs) in the surveillance region. It follows that the proposed approach utilises the sequential importance sampling filter for recursive target state estimation, in conjunction with a 2-D data assignment method for measurement-to-target association. Computer simulations are also included to demonstrate and evaluate the performance of the proposed approach.

In this paper, a system solution is designed to improve the service efficiency of intersection. For the purpose, a method of trajectory prediction for vehicle is put forward, which is based on the basis of realize multi-targets tracking. The characteristics of traffic video, structural of road profile and movement of vehicles have been analyzed. We designed the multi-targets tracking algorithm based on particle filter for traffic video sequence, in order to improve the accuracy and rapidity, the heuristic method for multi-targets data correlation is proposed. Based on the results of tracking, we analyze the rule of movement parameters of vehicle as the path has changed, according to the features of structured road the vehicle running status parameters is simplified, and a Hidden Markov Model (HMM) has trained, which is used for vehicle trajectory prediction. In the end, Analysis the basic reason of traffic conflict and the dynamic model of traffic conflict zone is designed. Experimental results demonstrate that, the method of tracking based on particle filter has good tracking performance in traffic video; the vehicle trajectory can be predicted rapidly in the required time, and traffic conflict zone is produced accurately.

Abstract: Amidst the rapid evolution of autonomous vehicle technologies, ensuring road safety remains a paramount challenge. Effective detection of lanes and potential hazards, including speed breakers and potholes, is critical for safe autonomous driving. In this study, we introduce an innovative Lane, Speed Breaker, and Pothole Detection System (LSPDS) utilizing YOLOv4 Tiny, a state-of-the-art object detection algorithm and computer vision techniques. Our system integrates computer vision and machine learning techniques for analysis of road conditions. By employing camera sensors, we capture the road scene and apply image processing algorithms to identify lanes, speed breakers, and potholes. Moreover, the system incorporates Firebase for user authentication and SMS services for real-time alerts. YOLOv4 Tiny is employed for accurate detection and classification of these features within the captured images, thereby enhancing the perception capabilities of autonomous vehicles.

Information is obtained from human eyes for thinking divergence, and further associated with computer equipment, so human beings endow computers with the ability of “vision” to convey and feel information. This field has developed for many years, and many aspects can be in line with other research directions, such as artificial intelligence, which has become popular in recent years, and pattern recognition, which has been applied a lot. In order to sort out the structure and content of multitarget recognition smoothly, this paper starts from the perspective of shallow vision, uses theory and practical experiments, and chooses the core technology with the largest weight from massive computer technologies, so that the recognition algorithm can compare with the recognition algorithm. The research shows that (1) CNN shows its unique feature ability and incomparable detection accuracy from many models, and the error rate can be reduced from 28.07% to 18.40%. (2) The method of candidate region is complex, and the larger the region, the more difficult it is to calculate. The method based on regression is far beyond it in both precision and speed and is more suitable for the research of this subject. (3) When the mAP increases, the speed is forced to slow down. If the image resolution is high with the same model, the mAP will be high (SSD and YOLO models are often used). Experiments show that the recognition effect is obvious. At the end of the article, the advantages and disadvantages of this study are summarized. In the field of computer vision, people need to do more in-depth research. Follow-up can optimize multitarget recognition and detection and strive to improve the accuracy.

Computer vision is a subdomain of artificial intelligence that permits computers to extract useful info from digital images and videos. Artificial Intelligence enables to think, but in Computer Vision enable to see, observe and understand Computer Vision includes many techniques to solve real time problems. This research work focuses on one among these techniques viz. object detection. A computer vision technology called object detection helps locate and identify objects in images. In this paper mainly focusing on Neural network-based object detection techniques and how these techniques will impact on real time applications.

Deep learning based computer vision approaches for smart agricultural applications

In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.

This study proposes a tracking system for the transportation field utilizing state-of-the-art detection heads and tracking algorithm. We utilize detection heads (YOLO5, FRCNN, DETR) with tracking algorithms (ByteTrack) under different camera settings and three weather scenarios. The tracking results are summarized regarding Recall and precision, IDF1, IDP, IDR, MOTA, MOTP, tracking consistency, and processing time. In this study, we utilize novel Transformer architecture, a deep learning network based on the attention mechanism. Due to its strong capabilities, attention architecture has recently attracted a huge amount of attention in computer vision. This analysis showed that DETR was outperforming other algorithms in terms of tracking consistency, and it was performing similarly to FRCNN in Recall with better performance at the camera installed on intersections. Both FRCNN and DETR were outperforming YOLO5 in terms of Recall. YOLO5 is almost outperforming other algorithms in terms of Precision, MOTA, IDF1, and inference time. Considering intersections and highways, this paper has practical significance in intelligent traffic management and control.