VBDA_TC_Poster1 - Copy.pdf

The traditional computer vision techniques are unable

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The traditional computer vision techniques are unable to analyze such a huge amount of visual data generated in real-time. So, there is a need for visual big data analytics which involves processing and analyzing large scale visual data such as images or videos to find semantic patterns that are useful for interpretation. In this work, we propose a framework for visual big data analytics for automatic detection of bike-riders without helmet in city traffic. We also discuss challenges involved in visual big data analytics for traffic control in a city scale surveillance data and explore opportunities for future research. Motivation Automatic systems to catch traffic violators are highly desirable. High risk is associated with two-wheelers. Observing the usefulness of helmet, Governments have made it a punishable offense to ride a bike without helmet. System relay on humans whose efficiency may decrease over a long duration are not a feasible [2]. Cost-effective: Smart cities using CCTV surveillance cameras at public places for round the clock security monitoring. Challenges Real-time implementation Occlusion Direction of motion Temporal changes in weather conditions Quality of video feed [1]. IITH_Helmet_1 Dataset Training: 42 bikes, 13 cars, 40 humans. Testing: 63 bikes, 25 cars, 66 humans. Figure 1: Sample frames from dataset. Detection of Bike-riders without Helmet Figure 2: Block Diagram. Consider y i { - 1, + 1 } be label for i th frame. If for past n frames, 1 n n i = 1 ( y i = 1 ) > T f , then framework triggers violation alarm. The value of T f = 0.8 and n = 10 Visual Big Data Analytics Framework
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