Multi-feature Fusion for Video Object Tracking

Song, Yuqing; Yue, Dongpeng

doi:10.1109/icinis.2012.56

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…This paper is an extension of our previous work [23]. The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 93%

Graph Grammar Based Object Detecting and Tracking

Song¹,

Yue²,

Mo³

2013

IJIES

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Abstract:In this paper we introduce a graph grammar based method to fuse the low level features and apply them to object detecting and tracking. In our algorithm, the graph grammar rules are used to detect the object in the beginning of the video sequence and then dynamically adjust the tracking procedure. Our tracking algorithm consists of two phases: key points tracking and tracking by graph grammar rules. The key points are computed by using salient level set components. All key points, as well as the colors and the tangent directions, are fed to a Kalman filter for object tracking. Then the graph grammar rules are used to dynamically examine and adjust the tracking procedure to make it robust. The effectiveness of the algorithm has been demonstrated by experiments.

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“…This paper is an extension of our previous work [23]. The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 93%

Graph Grammar Based Object Detecting and Tracking

Song¹,

Yue²,

Mo³

2013

IJIES

Self Cite

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show abstract

“…Then the data is correlated with the established multi-feature appearance model. Song [26] et al proposed a low level feature fusion algorithm based on graph grammar. In the stage of object detection, graph grammar rules are used to find significant horizontal components that constitute the object.…”

Section: Related Workmentioning

confidence: 99%

Research on student group tracking algorithm based on teaching scene

Gui

Shi

2022

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022)

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Expectations by computer vision technology to change traditional teaching model has become the research focus of many scholars, but due to lack of specific training data, and the classroom students intensive distribution, variety, easy to keep out each other, the existing multiple object detection and object tracking technique is applied to the poor performance of the teaching scene. For the above problems, in the stage of multi object detection, considering that the head can not only represent the multi-pose student object, but also is not easy to be completely blocked, this paper established a small dataset of teaching scenes with the head as the detection object to train and improve the accuracy of the Faster R-CNN detector. In the multi object tracking stage, the Kalman filter and Hungarian algorithm are used for tracking, and then the unmatched trajectory is remapped to the depth feature map based on the strong feature extraction ability of Faster R-CNN backbone neural network. Then, we correct the unmatched trajectory by fusing the depth feature information and historical trajectory position information of the object, and calculate the trajectory similarity, which improves the problems of identity switching and trajectory interruption caused by the change of object attitude and occlusion. The research of this paper is used in real teaching scenes, which can assist teachers to track students' status and the formed student object trajectory sequence, and can provide data input for subsequent classroom behavior analysis.

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Automatic Helmet (Object) Detection and Tracking the Riders Using Kalman Filter Technique

Vairamani

2023

Contributions to Environmental Sciences &Amp; Innovative Business Technology

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Multi-feature Fusion for Video Object Tracking

Cited by 3 publications

References 14 publications

Graph Grammar Based Object Detecting and Tracking

Graph Grammar Based Object Detecting and Tracking

Research on student group tracking algorithm based on teaching scene

Automatic Helmet (Object) Detection and Tracking the Riders Using Kalman Filter Technique

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