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<!DOCTYPE html>
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content="DashCop: Automated E-ticket Generation for Two-Wheeler Traffic Violations Using Dashcam Videos. The system processes vehicle-mounted dashcam videos to detect two-wheeler traffic violations.">
<meta name="keywords" content="dashcop, DashCop, DashCop: Automated E-ticket Generation for Two-Wheeler Traffic Violations Using Dashcam Videos, Automated E-ticket Generation for Two-Wheeler Traffic Violations Using Dashcam Videos, RideSafe, RideSafe-400, Automated E-ticket generation, Traffic violation detection, Motorized two-wheelers, Dashcam video analysis, Triple riding detection, Helmet compliance, Automated traffic monitoring, Road safety technology, Two-wheeler accident prevention, Association-based tracking, License plate recognition, AutomatedEnforcement, TrafficSafety, DashCamAnalysis, TwoWheelerSafety, TripleRiding, HelmetDetection, E-Ticketing, CrossAssociation, RideSafe400, RoadSafetyTech, LicenseRecognition, ViolationDetection, TrafficMonitoring, DynamicTracking, AccidentPrevention">
<meta name="author" content="Deepti Rawat">
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<title>DashCop: Automated E-ticket Generation for Two-Wheeler Traffic Violations Using Dashcam Videos</title>
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<h1 class="title is-3 publication-title">DashCop: Automated E-ticket Generation for Two-Wheeler Traffic Violations Using Dashcam Videos</h1>
<h2 class="title is-5 publlication-title">WACV 2025</h2>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://www.linkedin.com/in/dpt-xyz/&hl=en">Deepti Rawat</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/keshavgupta06/">Keshav Gupta</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/aryamaan-basu-roy/">Aryamaan Basu Roy</a>,
</span>
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<a href="https://ravika.github.io">Ravi Kiran Sarvadevabhatla</a>
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<span class="author-block">IIIT Hyderabad</span>
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<section class="hero teaser">
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<!-- teaser triple riding violation -->
<div class="column">
<div class="content">
<h2 class="title is-3">Triple Riding Violation</h2>
<p>
<i>DashCop</i> employs our novel Segmentation and Cross-Association (SAC) module to accurately detect and track multiple riders on a single motorcycle. The system can identify triple
riding violations from dashcam footage, even under challenging road conditions
and varying lighting.
</p>
<video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/tr_violation.mp4"
type="video/mp4">
</video>
</div>
</div>
<!-- teaser triple riding violation -->
<!-- teaser helmet rule violation -->
<div class="column">
<h2 class="title is-3">Helmet Rule Violation</h2>
<div class="columns is-centered">
<div class="column content">
<p>
Our system also detect riders without helmets, a critical safety violation. The cross-association-based tracking algorithm ensures
consistent monitoring of individual riders throughout the video sequence, enabling
accurate violation detection and E-ticket generation.
</p>
<video id="matting-video" controls playsinline height="100%">
<source src="./static/videos/helmet_violation.mp4"
type="video/mp4">
</video>
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<h2 class="title is-3">Abstract</h2>
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<p>
Motorized two-wheelers are a prevalent and economical means of transportation, particularly in the Asia-Pacific region. However, hazardous driving practices such as triple riding and non-compliance with helmet regulations contribute significantly to accident rates. Addressing these violations through automated enforcement mechanisms can enhance traffic safety.
</p>
<p>
In this paper, we propose <i>DashCop</i>, an end-to-end system for automated E-ticket generation. The system processes vehicle-mounted dashcam videos to detect two-wheeler traffic violations. Our contributions include: (1) a novel Segmentation and Cross-Association (SAC) module to accurately associate riders with their motorcycles, (2) a robust cross-association-based tracking algorithm optimized for the simultaneous presence of riders and motorcycles, and (3) the RideSafe-400 dataset, a comprehensive annotated dashcam video dataset for triple riding and helmet rule violations.
</p>
<p>
Our system demonstrates significant improvements in violation detection, validated through extensive evaluations on the <i>RideSafe-400</i> dataset.
</p>
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</div>
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<h2 class="title is-3">DashCop: Methodology</h2>
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frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
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<h2 class="title is-3">DashCop: Unified Interface</h2>
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<iframe src="https://www.youtube.com/embed/0QRPPqrfHgQ?rel=0&showinfo=0"
frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
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<h2 class="title is-3">Comparison</h2>
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<!-- Left Column -->
<div class="column">
<div class="content">
<h2 class="title is-4">Triple Riding Violation Detection</h2>
<p>
Our SAC module improves rider-motorcycle association by learning to detect cross-object class, outperforming IOU-based methods. The joint tracking approach reduces false positives in crowded scenes.
</p>
<div class="has-text-centered">
<img src="./static/images/tr_comparison.png"
alt="Evaluation of triple riding violation detection"
class="comparison-image"/>
</div>
</div>
</div>
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<h2 class="title is-4">Helmet Rule Violation Detection</h2>
<p>
Frame-level detection approach achieves superior F1-scores in helmet violation detection, demonstrating better balance across different ROI extraction methods compared to existing solutions.
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<div class="has-text-centered">
<img src="./static/images/hv_comparison.png"
alt="Evaluation of helmet rule violation detection"
class="comparison-image"/>
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<h2 class="title is-4">Rider-Motorcycle Association</h2>
<p>
Our SAC module achieves superior association accuracy compared to geometric heuristic methods by learning directly in image space rather than relying solely on bounding box coordinates.
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<img src="./static/images/RM_assoc_comparison.png"
alt="Evaluation of rider-motorcycle association"
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<h2 class="title is-4">Rider-Motorcycle Instance Tracking</h2>
<p>
Joint tracking of rider-motorcycle instances demonstrates better performance across HOTA, MOTA, and IDF1 metrics compared to independent tracking and post-hoc aggregation approaches.
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<img src="./static/images/tracking_comparison.png"
alt="Evaluation of rider-motorcycle instance tracking"
class="comparison-image"/>
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<h2 class="title is-4">Evaluation Criteria for E-ticket Generation</h2>
<p>
For a given R-M track, the labelling (TP, FP, FN) at various stages of system is used to determine the final E-ticket level label of the track. E.g., a True Positive (TP) prediction at all stages is considered a TP for E-ticket generation system.
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<img src="./static/images/eval_criteria.png"
alt="Evaluation of e-ticket generation"
class="comparison-image"/>
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<h2 class="title is-4">Overall E-ticket System Performance</h2>
<p>
The automated system achieves an F1-score of 72.18%, reflecting good overall performance. In practice, traffic enforcement personnel review E-Tickets and corresponding evidence before issuance. This human-in-the-loop approach eliminates false positives, raising the F1-score to 82.05%, improving system reliability.
</p>
<div class="has-text-centered">
<img src="./static/images/overall_perf.png"
alt="Overall E-ticket system performance"
class="comparison-image"/>
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References: Goyal <i>et al.</i> <a href="https://arxiv.org/abs/2204.08364">[21]</a>; Cui <i>et al.</i> <a href="https://openaccess.thecvf.com/content/CVPR2023W/AICity/papers/Cui_An_Effective_Motorcycle_Helmet_Object_Detection_Framework_for_Intelligent_Traffic_CVPRW_2023_paper.pdf">[16]</a>; YOLOv8-x <a href="https://docs.ultralytics.com/models/yolov8/">[28]</a>; ByteTrack <a href="https://arxiv.org/abs/2110.06864">[62]</a>; DeepOCSORT <a href="https://arxiv.org/abs/2302.11813">[37]</a>; BotSORT <a href="https://arxiv.org/abs/2206.14651">[4]</a>; HybridSORT <a href="https://arxiv.org/html/2308.00783">[59]</a>.
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<h2 class="title is-3 mt-5">Our Dataset: RideSafe-400</h2>
<h3 class="title is-4">Triple Riding and Helmet Rule Violators Captured in Diverse Scenarios</h3>
<div class="content has-text-justified">
<p>
The RideSafe-400 dataset captures a wide range of real-world scenarios that challenge traffic violation detection systems. These include crowded scenes with multiple motorcycles, varying lighting conditions, partial occlusions, and diverse viewing angles. The dataset features comprehensive annotations for triple riding violations and helmet rule compliance across a range of urban and suburban environments.
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<img src="./static/images/challenging_scenarios_blurredface_resize.png"
alt="RideSafe-400 Dataset showing various challenging scenarios of traffic violations"
style="width: 100%; height: auto; display: block;" loading="lazy" /> <!-- Force full width -->
<figcaption style="margin-top: 1rem;">Examples from RideSafe-400 Dataset</figcaption>
</figure>
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<h3 class="title is-4">Statistics</h3>
<div class="content has-text-justified">
<p>
We provide both frame-level and track-level annotations for rider-motorcycle pairs, triple riding instances, and helmet violations. The dataset includes detailed license plate annotations with format information, enabling comprehensive evaluation of our automated E-ticket generation system. These annotations support thorough evaluation of detection, tracking, and violation recognition tasks.
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<figure style="margin: 0; padding: 0; width: 100%;"> <!-- Added full width and removed margins -->
<img src="./static/images/distribution_classes_and_lp.png"
alt="RideSafe-400 Statistics"
style="width: 100%; height: auto; display: block;" loading="lazy" /> <!-- Force full width -->
<figcaption style="margin-top: 1rem;"><b>Top-Left</b>: Frame-level annotations for each object category. <b>Bottom-Left</b>: Track-level annotations for each object category. <b>Top-Right</b>: License plate number (LPN) attribute labelling format. <b>Bottom-Right</b>: License plate annotation distribution.</figcaption>
</figure>
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<h2 class="title is-3">Download the Dataset</h2>
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Coming Soon!
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<h2 class="title">BibTeX</h2>
<pre>
<code>
@InProceedings{Rawat_2025_WACV,
author = {Rawat, Deepti and Gupta, Keshav and Roy, Aryamaan Basu and Sarvadevabhatla, Ravi Kiran},
title = {DashCop: Automated E-Ticket Generation for Two-Wheeler Traffic Violations using Dashcam Videos},
booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)},
month = {February},
year = {2025},
pages = {5387-5397}
}
</code>
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<h2 class="title">Reach out at</h2>
<pre><code>deepti.rawat@research.iiit.ac.in</code></pre>
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