Real-Time Tracking Based on Keypoints and Discriminative Correlation Filters

AbstractWith the increasing application of computer vision technology in autonomous driving, robot, and other mobile devices, more and more attention has been paid to the implementation of target detection and tracking algorithms on embedded platforms. The real-time performance and robustness of algorithms are two hot research topics and challenges in this field. In order to solve the problems of poor real-time tracking performance of embedded systems using convolutional neural networks and low robustness of tracking algorithms for complex scenes, this paper proposes a fast and accurate real-time video detection and tracking algorithm suitable for embedded systems. The algorithm combines the object detection model of single-shot multibox detection in deep convolution networks and the kernel correlation filters tracking algorithm, what is more, it accelerates the single-shot multibox detection model using field-programmable gate arrays, which satisfies the real-time performance of the algorithm on the embedded platform. To solve the problem of model contamination after the kernel correlation filters algorithm fails to track in complex scenes, an improvement in the validity detection mechanism of tracking results is proposed that solves the problem of the traditional kernel correlation filters algorithm not being able to robustly track for a long time. In order to solve the problem that the missed rate of the single-shot multibox detection model is high under the conditions of motion blur or illumination variation, a strategy to reduce missed rate is proposed that effectively reduces the missed detection. The experimental results on the embedded platform show that the algorithm can achieve real-time tracking of the object in the video and can automatically reposition the object to continue tracking after the object tracking fails.

Download Full-text

Real-time tracking of multiple objects with locally adaptive correlation filters

10.18287/1613-0073-2017-1901-214-218 ◽

2017 ◽

Author(s):

Alexey N. Ruchay ◽

◽

Vitaly I. Kober ◽

Ilya E. Chernoskulov ◽

◽

...

Keyword(s):

Real Time ◽

Correlation Filters ◽

Multiple Objects ◽

Locally Adaptive ◽

Real Time Tracking

Download Full-text

An Overview of Data Aggregation Architecture for Real-Time Tracking with Sensor Networks

10.21236/ada446929 ◽

2006 ◽

Cited By ~ 8

Author(s):

Tian He ◽

Lin Gu ◽

Liqian Luo ◽

Ting Yan ◽

John A. Stankovic ◽

...

Keyword(s):

Sensor Networks ◽

Real Time ◽

Data Aggregation ◽

Real Time Tracking

Download Full-text

A Sequential Monte Carlo Method for Real-time Tracking of Multiple Targets

10.21236/ada532576 ◽

2010 ◽

Author(s):

Scott T. Acton ◽

Bing Li

Keyword(s):

Monte Carlo ◽

Monte Carlo Method ◽

Real Time ◽

Sequential Monte Carlo ◽

Multiple Targets ◽

Sequential Monte Carlo Method ◽

Real Time Tracking

Download Full-text

Enhancing spatial perception through sound: mapping human movements into MIDI

Multimedia Tools and Applications ◽

10.1007/s11042-021-11077-7 ◽

2021 ◽

Author(s):

Bernardo Breve ◽

Stefano Cirillo ◽

Mariano Cuofano ◽

Domenico Desiato

Keyword(s):

Real Time ◽

User Study ◽

Spatial Perception ◽

Human Movements ◽

Different Types ◽

Real Time Tracking ◽

People’S Perceptions ◽

Novel Algorithm

AbstractGestural expressiveness plays a fundamental role in the interaction with people, environments, animals, things, and so on. Thus, several emerging application domains would exploit the interpretation of movements to support their critical designing processes. To this end, new forms to express the people’s perceptions could help their interpretation, like in the case of music. In this paper, we investigate the user’s perception associated with the interpretation of sounds by highlighting how sounds can be exploited for helping users in adapting to a specific environment. We present a novel algorithm for mapping human movements into MIDI music. The algorithm has been implemented in a system that integrates a module for real-time tracking of movements through a sample based synthesizer using different types of filters to modulate frequencies. The system has been evaluated through a user study, in which several users have participated in a room experience, yielding significant results about their perceptions with respect to the environment they were immersed.

Download Full-text

Real-time tracking lysosomal pH changes under heatstroke and redox stress with a novel near-infrared emissive probe

Talanta ◽

10.1016/j.talanta.2021.122184 ◽

2021 ◽

Vol 228 ◽

pp. 122184

Author(s):

Qingfeng Xia ◽

Shumin Feng ◽

Jiaxin Hong ◽

Guoqiang Feng

Keyword(s):

Real Time ◽

Near Infrared ◽

Redox Stress ◽

Lysosomal Ph ◽

Ph Changes ◽

Real Time Tracking ◽

Emissive Probe

Download Full-text

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219086 ◽

2021 ◽

pp. 1-10

Author(s):

Lipeng Si ◽

Baolong Liu ◽

Yanfang Fu

Keyword(s):

Neural Network ◽

Real Time ◽

Image Recognition ◽

Error Detection ◽

Convolution Neural Network ◽

Data Sets ◽

Small Target ◽

Network Algorithm ◽

Neural Network Algorithm ◽

Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

Download Full-text