Multiple object tracking using feature fusion in hierarchical LSTMs

Hassan, Ehtesham

doi:10.1049/joe.2020.0111

Cited by 1 publication

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing work captures the viewpoint of objects within the detected bounding box for tracking. His proposed motion representation and deep features representing object appearance are fused together [4]. Existing convolutional neural network-(CNN-) based trackers have limited tracking performance because the features extracted from single-layer or multilayer linear combinations are insufficient to describe the object appearance.…”

Section: Related Workmentioning

confidence: 99%

Multifeature Fusion Human Pose Tracking Algorithm Based on Motion Image Analysis

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Human pose and motion detection is an important area of computer vision research, covering the interplay of different fields such as image processing, pattern recognition, and artificial intelligence. Due to the complexity of human motion, existing 3D recognition and pose detection methods based on low-quality depth images are not very accurate and reliable. Due to the time-sensitive nature of features, a single feature cannot adapt to the dynamic changes of the scene, so it is difficult for the target tracking algorithm based on a single feature to achieve robust tracking results. If multiple features are fused and applied in the tracking algorithm, the complementarity between different features can be used to better adapt to the scene changes and achieve robust tracking results. In order to solve the problem of human pose and human motion recognition in low-quality depth images, this paper uses the Kinect somatosensory camera to obtain 20 human skeleton joint points through the Kinect skeleton tracking technology. This paper studies the typical human posture and interactive action recognition technology in daily life. On the basis of understanding the characteristics of skeletal data, this paper proposes a distance feature and angle feature model combined with human body structure. Through the experimental results, it is found that the distance feature and the angle feature value are basically not affected by the distance change. When the subjects turned 45° to the left and 45° to the right, the distance characteristics changed, which was different from the characteristic data when the subjects turned around.

show abstract

Section: Related Workmentioning

confidence: 99%

Multifeature Fusion Human Pose Tracking Algorithm Based on Motion Image Analysis

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

Multiple object tracking using feature fusion in hierarchical LSTMs

Cited by 1 publication

References 29 publications

Multifeature Fusion Human Pose Tracking Algorithm Based on Motion Image Analysis

Multifeature Fusion Human Pose Tracking Algorithm Based on Motion Image Analysis

Contact Info

Product

Resources

About