TF-SMOT composes pretrained vision-language models into a training-free pipeline that reaches state-of-the-art tracking and improved summary quality on the BenSMOT benchmark.
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An adaptive ROI selection framework for video vehicle counting that combines detection, tracking, and density models to reach up to 100% accuracy and four times faster processing on standard traffic datasets.
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Training-Free Semantic Multi-Object Tracking with Vision-Language Models
TF-SMOT composes pretrained vision-language models into a training-free pipeline that reaches state-of-the-art tracking and improved summary quality on the BenSMOT benchmark.
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Intelligent ROI-Based Vehicle Counting Framework for Automated Traffic Monitoring
An adaptive ROI selection framework for video vehicle counting that combines detection, tracking, and density models to reach up to 100% accuracy and four times faster processing on standard traffic datasets.