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

Sami Abdulla Mohsen Saleh, A. Halim Kadarman (), Shahrel Azmin Suandi (), Sanaa A. A. Ghaleb, Waheed A. H. M. Ghanem, Solehuddin Shuib and Qusay Shihab Hamad
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Sami Abdulla Mohsen Saleh: Intelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia
A. Halim Kadarman: School of Aerospace Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia
Shahrel Azmin Suandi: Intelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia
Sanaa A. A. Ghaleb: Faculty of Computing and Informatics, Universiti Sultan Zainal Abidin, Kampung Gong Badak 21300, Terengganu, Malaysia
Waheed A. H. M. Ghanem: Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Terengganu 21030, Terengganu, Malaysia
Solehuddin Shuib: Faculty of Mechanical Engineering, Universiti Teknologi Mara, Shah Alam 40450, Selangor, Malaysia
Qusay Shihab Hamad: Intelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia

Mathematics, 2023, vol. 11, issue 5, 1-21

Abstract: 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.

Keywords: crowd analysis; coherent motion detection; trajectory clustering; KLT tracklets (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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