Fast Visual Object Tracking Using Modified kalman and Particle Filtering Algorithms in the Presence of Occlusions

Rao, Guoyao; Nandyala, Siva Prasad; Satyanarayana, Ch.

doi:10.5815/ijigsp.2014.10.06

Cited by 4 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For vehicle tracking, the distance characteristics of objects between adjacent frames are commonly used in mainstream methods, such as the Hungarian method [21]. Predictive tracking algorithms using Kalman and particle filtering are also another major type of method [22][23][24].…”

Section: Vehicle Detection and Tracking Using In-vehicle Lidarmentioning

confidence: 99%

Vehicle Detection and Tracking with Roadside LiDAR Using Improved ResNet18 and the Hungarian Algorithm

Lin,

Sun,

et al. 2023

Sensors

View full text Add to dashboard Cite

By the end of the 2020s, full autonomy in autonomous driving may become commercially viable in certain regions. However, achieving Level 5 autonomy requires crucial collaborations between vehicles and infrastructure, necessitating high-speed data processing and low-latency capabilities. This paper introduces a vehicle tracking algorithm based on roadside LiDAR (light detection and ranging) infrastructure to reduce the latency to 100 ms without compromising the detection accuracy. We first develop a vehicle detection architecture based on ResNet18 that can more effectively detect vehicles at a full frame rate by improving the BEV mapping and the loss function of the optimizer. Then, we propose a new three-stage vehicle tracking algorithm. This algorithm enhances the Hungarian algorithm to better match objects detected in consecutive frames, while time–space logicality and trajectory similarity are proposed to address the short-term occlusion problem. Finally, the system is tested on static scenes in the KITTI dataset and the MATLAB/Simulink simulation dataset. The results show that the proposed framework outperforms other methods, with F1-scores of 96.97% and 98.58% for vehicle detection for the KITTI and MATLAB/Simulink datasets, respectively. For vehicle tracking, the MOTA are 88.12% and 90.56%, and the ID-F1 are 95.16% and 96.43%, which are better optimized than the traditional Hungarian algorithm. In particular, it has a significant improvement in calculation speed, which is important for real-time transportation applications.

show abstract

Section: Vehicle Detection and Tracking Using In-vehicle Lidarmentioning

confidence: 99%

Vehicle Detection and Tracking with Roadside LiDAR Using Improved ResNet18 and the Hungarian Algorithm

Lin,

Sun,

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…DRLS algorithm has faster convergence speed but this system is more complex [19].PSO is based on swarm intelligence. In PSO algorithm considers movement of group of birds, bees, school of fishes [20].The Kalman filter (KF) estimates unknown variables with precise values based on measurements over a time [21]. It has numerous applications in the field of signal processing,marine navigation, control of vehicles, aerospace.…”

Section: Existing Solutionsmentioning

confidence: 99%

Enhancement of Indoor Localization in WSN using PSO tuned EKF

Janapati¹,

Balaswamy²,

Soundararajan³

2017

IJISA

View full text Add to dashboard Cite

In Wireless Sensor Networks, nodes are positioned arbitrarily and finding location of nodes is difficult. In this network, the nodes need to know their location is important for indoor applications. In this applications signals are affected by various factors such as noise, multipath, NLOS etc. This impact on inaccurate location information of node, which leads finding path to the destination node is difficult. Cooperative location based routing is alternative solution for finding better path. In this paper a solution is proposed for effective route in indoor application of WSN. The proposed solution uses Particle Swarm Optimization assisted Adaptive Extended Kalman Filter (PSO-AKF) for finding location of nodes. In this mechanism, finding accurate position of node impact on network performance such as minimization of delay, location error and also minimizes complexity.

show abstract

“…Most research achievements in the visual tracking field have blossomed over the past few decades, divided into two branches called generative model [12][13][14] and discriminative model [15,16]. The generative approaches establish models or templates with respect to the area of target in the current frame, aim at describing the targets' appearance and find the most similar areas in the next frame as the estimated new position (e.g., Kalman filtering [17], Particle filtering [18], Mean-shift [19], etc.).…”

Section: Introductionmentioning

confidence: 99%

Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator

et al. 2021

View full text Add to dashboard Cite

The cable-driven hyper-redundant snake-like manipulator (CHSM) inspired by the biomimetic structure of vertebrate muscles and tendons, which consists of numerous joint units connected adjacently driven by elastic materials with hyper-redundant DOF, performs flexible kinematic skills and competitive compound capability under complicated working circumstances. Nevertheless, the drawback of lacking the ability to perceive the environment to perform intelligently in complex scenarios leaves a lot to be improved, which is the original intention to introduce visual tracking feedback acting as an instructor. In this paper, a cable-driven snake-like robotic arm combined with a visual tracking technique is introduced. A visual tracking approach based on dual correlation filter is designed to guide the CHSM in detecting the target and tracing after its trajectory. Specifically, it contains an adaptive optimization for the scale variation of the tracking target via pyramid sampling. For the CHSM, an explicit kinematics model is derived from its specific geometry relationships and followed by a simplification for the inverse kinematics based on some assumption or limitation. A control scheme is brought up to combine the kinematics with visual tracking via the processing tracking errors. The experimental results with a practical prototype validate the availability of the proposed compound control method with the derived kinematics model.

show abstract

Fast Visual Object Tracking Using Modified kalman and Particle Filtering Algorithms in the Presence of Occlusions

Cited by 4 publications

References 6 publications

Vehicle Detection and Tracking with Roadside LiDAR Using Improved ResNet18 and the Hungarian Algorithm

Vehicle Detection and Tracking with Roadside LiDAR Using Improved ResNet18 and the Hungarian Algorithm

Enhancement of Indoor Localization in WSN using PSO tuned EKF

Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator

Contact Info

Product

Resources

About