A Tracklet-before-Clustering Initialization Strategy Based on Hierarchical KLT Tracklet Association for Coherent Motion Filtering Enhancement

Coherent motions depict the individuals’ collective movements in widely existing moving crowds in physical, biological, and other systems. In recent years, similarity-based clustering algorithms, particularly the Coherent Filtering (CF) clustering approach, have accomplished wide-scale popularity an...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-03, Vol.11 (5), p.1075
Main Authors: Saleh, Sami Abdulla Mohsen, Kadarman, A. Halim, Suandi, Shahrel Azmin, Ghaleb, Sanaa A. A., Ghanem, Waheed A. H. M., Shuib, Solehuddin, Hamad, Qusay Shihab
Format: Article
Language:English
Subjects:
Algorithms
Behavior
Clustering
Coherence
coherent motion detection
crowd analysis
Deep learning
Filtration
KLT tracklets
Mathematical research
Motion perception
Pattern recognition
Surveillance
Teaching methods
trajectory clustering
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Summary:Coherent motions depict the individuals’ collective movements in widely existing moving crowds in physical, biological, and other systems. In recent years, similarity-based clustering algorithms, particularly the Coherent Filtering (CF) clustering approach, have accomplished wide-scale popularity and acceptance in the field of coherent motion detection. In this work, a tracklet-before-clustering initialization strategy is introduced to enhance coherent motion detection. Moreover, a Hierarchical Tracklet Association (HTA) algorithm is proposed to address the disconnected KLT tracklets problem of the input motion feature, thereby making proper trajectories repair to optimize the CF performance of the moving crowd clustering. The experimental results showed that the proposed method is effective and capable of extracting significant motion patterns taken from crowd scenes. Quantitative evaluation methods, such as Purity, Normalized Mutual Information Index (NMI), Rand Index (RI), and F-measure (Fm), were conducted on real-world data using a huge number of video clips. This work has established a key, initial step toward achieving rich pattern recognition.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11051075