Mining Platoon Patterns from Traffic Videos

2024-12-28

AI Summary

Urban traffic video analysis suffers from occlusion, detection failures, and discontinuous camera coverage, severely hindering robust platoon identification. To address this, we propose a robust co-traveling pattern mining method. We introduce a relaxed definition of “co-traveling” that tolerates trajectory segment gaps, enabling resilience to incomplete tracking. We design a verification-free MaxGrowth enumeration framework, integrating sequential cluster modeling with a two-stage pruning strategy to efficiently discover maximal co-traveling patterns directly from imperfect video trajectories—guaranteeing zero false positives. Evaluated on real-world traffic video datasets, our algorithm achieves up to two orders of magnitude speedup over baseline methods while maintaining high precision and completeness. This significantly enhances the practicality and scalability of city-scale platoon detection, offering a reliable foundation for intelligent traffic management systems.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Computer Vision: Motion & Tracking
• Search and Optimization: Combinatorial Optimization

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Discovering co-movement patterns from urban-scale video data sources has emerged as an attractive topic. This task aims to identify groups of objects that travel together along a common route, which offers effective support for government agencies in enhancing smart city management. However, the previous work has made a strong assumption on the accuracy of recovered trajectories from videos and their co-movement pattern definition requires the group of objects to appear across consecutive cameras along the common route. In practice, this often leads to missing patterns if a vehicle is not correctly identified from a certain camera due to object occlusion or vehicle mis-matching. To address this challenge, we propose a relaxed definition of co-movement patterns from video data, which removes the consecutiveness requirement in the common route and accommodates a certain number of missing captured cameras for objects within the group. Moreover, a novel enumeration framework called MaxGrowth is developed to efficiently retrieve the relaxed patterns. Unlike previous filter-and-refine frameworks comprising both candidate enumeration and subsequent candidate verification procedures, MaxGrowth incurs no verification cost for the candidate patterns. It treats the co-movement pattern as an equivalent sequence of clusters, enumerating candidates with increasing sequence length while avoiding the generation of any false positives. Additionally, we also propose two effective pruning rules to efficiently filter the non-maximal patterns. Extensive experiments are conducted to validate the efficiency of MaxGrowth and the quality of its generated co-movement patterns. Our MaxGrowth runs up to two orders of magnitude faster than the baseline algorithm. It also demonstrates high accuracy in real video dataset when the trajectory recovery algorithm is not perfect.

Problem

• Object Tracking
• Urban Traffic Management
• Smart City Construction

Innovation

• MaxGrowth Algorithm
• Incomplete Trajectory Recognition
• Optimization Strategies for Pattern Detection