Combining YOLO and Visual Rhythm for Vehicle Counting

2023-11-06

Anais Estendidos da XXXVI Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2023)

AI Summary

To address the high computational cost and poor real-time performance of vehicle counting in video surveillance, this paper proposes a single-stage, tracking-free counting method. The core innovation lies in the first integration of YOLO-based object detection with Visual Rhythm—a temporal-spatial imaging technique—that encodes vehicle trajectories into static images. Detection is performed only on adaptively selected keyframes, and counting is achieved directly via spatial pattern analysis of the resulting Visual Rhythm image. This paradigm eliminates the conventional two-stage “detection + tracking” pipeline, drastically reducing computational overhead. The method exhibits strong generalizability to arbitrary unidirectional motion counting scenarios. Evaluated on real-world video datasets, it achieves an average counting accuracy of 99.15% and processes videos three times faster than state-of-the-art tracking-based approaches.

Technology Category

• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Video-based vehicle detection and counting play a critical role in managing transport infrastructure. Traditional image-based counting methods usually involve two main steps: initial detection and subsequent tracking, which are applied to all video frames, leading to a significant increase in computational complexity. To address this issue, this work presents an alternative and more efficient method for vehicle detection and counting. The proposed approach eliminates the need for a tracking step and focuses solely on detecting vehicles in key video frames, thereby increasing its efficiency. To achieve this, we developed a system that combines YOLO, for vehicle detection, with Visual Rhythm, a way to create time-spatial images that allows us to focus on frames that contain useful information. Additionally, this method can be used for counting in any application involving unidirectional moving targets to be detected and identified.Experimental analysis using real videos shows that the proposed method achieves mean counting accuracy around 99.15% over a set of videos, with a processing speed three times faster than tracking based approaches.

Problem

• Vehicle Detection
• Accuracy Improvement
• Efficient Counting

Innovation

• YOLO
• Visual Rhythm
• Efficient Vehicle Counting