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Pedestrian detection is an essential and significant task in any intelligent video surveillance system, as it provides the fundamental information for semantic understanding of the video footages. It has an obvious extension to automotive applications due to the potential for improving safety systems.
Pedestrian crash avoidance mitigation (PCAM) systems (USDOT Volpe Center [1]), also known as pedestrian protection or detection systems, use computer and artificial intelligence technology to recognize pedestrians and bicycles in an automobile's path to take action for safety.
The sequence of events in a car-pedestrian crash. Many pedestrian crashes involve a forward moving car (as opposed to buses and other vehicles with a vertical hood/bonnet). In such a crash, a standing or walking pedestrian is struck and accelerated to the speed of the car and then continues forward as the car brakes to a halt.
The number of people inside vehicles who were killed in traffic crashes fell from 32,043 in 2001 to 26,325 in 2021. But pedestrian deaths rose 51% to 7,388 during the same period, so improvements are needed, the agency said. Last year nearly 41,000 people died in traffic crashes nationwide, a 3.6% decline over 2022, according to NHTSA estimates ...
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1] Well-researched domains of object detection include face detection and pedestrian detection.
2018: Toyota released its updated Toyota Safety Sense 2.0 (TSS 2.0) to include Lane Tracing Assist, Road Sign Assist, and Low Light Pedestrian Detection with Daytime Bicyclist Detection which improves the Pre-Collision System. The first Japanese car model to receive (TSS 2.0) is the executive Crown in its 15th generation.
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