On the use of a minimal path approach for target trajectory analysis

Cancela, Brais; Penedo, Manuel G.; Novo, Jorge; Barreira, N.

doi:10.1016/j.patcog.2013.01.013

Cited by 7 publications

(10 citation statements)

References 30 publications

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“…Finally, in order to evaluate the overall performance of the algorithm, the recognition accuracy was used as the evaluation index, and the results were compared with those of references [2,3,10,14] on UCF sports and YouTubes datasets, respectively. The comparison results are shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

“…It is widely used in human-computer interactions, video surveillance, motion analysis, virtual reality, and other fields. Usually, the realization of video human behavior mainly includes two steps: one is feature extraction based on video information, the other is video classification based on feature vectors; however, because of the problems of background interference, partial occlusion, and different viewpoints, it is still a challenging task to obtain efficient and accurate motion representation [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

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Human Action Recognition Based on Foreground Trajectory and Motion Difference Descriptors

Dong

Daidi

et al. 2019

Applied Sciences

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Aimed at the problems of high redundancy of trajectory and susceptibility to background interference in traditional dense trajectory behavior recognition methods, a human action recognition method based on foreground trajectory and motion difference descriptors is proposed. First, the motion magnitude of each frame is estimated by optical flow, and the foreground region is determined according to each motion magnitude of the pixels; the trajectories are only extracted from behavior-related foreground regions. Second, in order to better describe the relative temporal information between different actions, a motion difference descriptor is introduced to describe the foreground trajectory, and the direction histogram of the motion difference is constructed by calculating the direction information of the motion difference per unit time of the trajectory point. Finally, a Fisher vector (FV) is used to encode histogram features to obtain video-level action features, and a support vector machine (SVM) is utilized to classify the action category. Experimental results show that this method can better extract the action-related trajectory, and it can improve the recognition accuracy by 7% compared to the traditional dense trajectory method.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Action Recognition Based on Foreground Trajectory and Motion Difference Descriptors

Dong

Daidi

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…When trying to test any trajectory analysis, the same problem arises: there is a total absence of ground truth information, except in the BARD dataset [5]. However, this dataset is too small (only over 600 trajectories) and does not have any time information included.…”

Section: Resultsmentioning

confidence: 99%

“…In [5], the minimal path is ruled by a potential image that contains, for every node, the number of people that reach it. However, in our algorithm, we also include the velocity of the targets.…”

Section: Hypothesis 2 the Trajectory Used To Reach The Goal Is Ruled mentioning

confidence: 99%