Real-time Tracking Of Moving Objects Research Articles

Real-Time Moving Object Tracking on Smartphone Using Cradle Head Servo Motor.

The increasing demand for artificially intelligent smartphone cradles has prompted the need for real-time moving object detection. Real-time moving object tracking requires the development of algorithms for instant tracking analysis without delays. In particular, developing a system for smartphones should consider different operating systems and software development environments. Issues in current real-time moving object tracking systems arise when small and large objects coexist, causing the algorithm to prioritize larger objects or struggle with consistent tracking across varying scales. Fast object motion further complicates accurate tracking and leads to potential errors and misidentification. To address these issues, we propose a deep learning-based real-time moving object tracking system which provides an accuracy priority mode and a speed priority mode. The accuracy priority mode achieves a balance between the high accuracy and speed required in the smartphone environment. The speed priority mode optimizes the speed of inference to track fast-moving objects. The accuracy priority mode incorporates CSPNet with ResNet to maintain high accuracy, whereas the speed priority mode simplifies the complexity of the convolutional layer while maintaining accuracy. In our experiments, we evaluated both modes in terms of accuracy and speed.

Marine Object Segmentation and Tracking by Learning Marine Radar Images for Autonomous Surface Vehicles

This paper proposes a radar image segmentation and tracking method by learning radar images for autonomous surface vehicles. To identify marine objects from radar images, we propose a deep neural network named the dual path squeeze and excitation network (DPSE-Net). By learning the radar images, the proposed DPSE-Net is designed to segment every pixel of the radar images into four classes: marine objects, land, noise, and background. The proposed DPSE-Net shows the best performance in radar image segmentation while operating in real-time, compared to state-of-the-art real-time image segmentation network models. In addition, we design a real-time moving object tracking algorithm for estimating the position and velocity of marine objects based on deep simple online real-time tracking with a deep association metric (DeepSORT), a widely used tracking algorithm. The existing DeepSORT algorithm uses the intersection over union (IoU) metric and a deep appearance descriptor for data association, but since they are not suitable for radar images, successive tracking is difficult. To solve this problem, a new data association metric suitable for radar images is proposed. The field tests in ocean environments confirm that the proposed method performs better in marine object segmentation and tracking.

Effective moving object tracking using modified flower pollination algorithm for visible image sequences under complicated background

In real time moving object tracking, various unconstraint factors such as severe occlusion and background clutter remain a challenge for developing an effective yet robust tracking method. In this study, a novel moving object tracking algorithm based on the modified flower pollination algorithm (MFPA) is proposed. A search window with the features of centroid coordinates and width of the search window is used to locate the position of the moving object. The sub-image within the search window is then extracted. The hue, saturation and value (HSV) histogram of the extracted region is calculated in order to model the object appearance representation. Subsequently, the Bhattacharyya distance between the HSV histograms from two consecutive frames is formulated as the fitness function of MFPA, in which maximizing the similarity of both histograms is sought after. The comparative experimental results show that the developed algorithm outperforms others in terms of efficiency and accuracy.

Phase-Difference Compensation and Nonuniform Pulse Transmission for Accurate Real-Time Moving Object Tracking

This paper presents a radio-based real-time moving object tracking method based on Kalman filtering using a phase-difference compensation technique and a non-uniform pulse transmission scheme. Conventional Kalman-based tracking methods often require time, amplitude, phase information and their derivatives for each receiver antenna; however, their location estimation accuracy does not become good even with many transmitting pulses. The presented method employs relative phase-difference information and a non-uniform pulse generation scheme, which can greatly reduce the number of transmitting pulses while preserving the tracking accuracy. Its performance is evaluated in comparison with that of conventional methods.

Efficient multi-feature PSO for fast gray level object-tracking

Robust and real-time moving object tracking is a tricky job in computer vision systems. The development of an efficient yet robust object tracker faces several obstacles, namely: dynamic appearance of deformable or articulated targets, dynamic backgrounds, variation in image intensity, and camera (ego) motion. In this paper, a novel tracking algorithm based on particle swarm optimization (PSO) method is proposed. PSO is a population-based stochastic optimization algorithm modeled after the simulation of the social behavior of bird flocks and animal hordes. In this algorithm, a multi-feature model is proposed for object detection to enhance the tracking accuracy and efficiency. The object's model is based on the gray level intensity. This model combines the effects of different object cases including zooming, scaling, rotating, etc. into a single cost function. The proposed algorithm is independent of object type and shape and can be used for many object tracking applications. Over 30 video sequences and having over 20,000 frames are used to test the developed PSO-based object tracking algorithm and compare it to classical object tracking algorithms as well as previously published PSO-based tracking algorithms. Our results demonstrate the efficiency and robustness of our developed algorithm relative to all other tested algorithms.

모바일 플랫폼을 위한 히스토그램 기반 객체추적

In this paper we propose a real-time moving object tracking method on a smart phone camera. By considering the limit of non-learning approach on low-performance platforms, we use the sliding-window detection technique based on histogram features. We solve the problem of the time-consuming histogram computation on each sub-window by adapting the integral histogram. For additional speed and tracking performance, we propose a new adaptive bin method. From the experiments on our dataset, we achieved high speed performance demonstrating 34~63 frames per second.

Specified Object Tracking Problem in an Environment of Multiple Moving Objects

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.