Abstract
Pedestrian detection is an important aspect of autonomous vehicle driving as recognizing pedestrians helps in reducing accidents between the vehicles and the pedestrians. In literature, feature based approaches have been mostly used for pedestrian detection. Features from different body portions are extracted and analyzed for interpreting the presence or absence of a person in a particular region in front of car. But these approaches alone are not enough to differentiate humans from non-humans in dynamic environments, where background is continuously changing. We present an automated pedestrian detection system by finding pedestrians’ motion patterns and combing them with HOG features. The proposed scheme achieved 17.7% and 14.22% average miss rate on ETHZ and Caltech datasets, respectively.
Highlights
According to National Highway Traffic Safety Administration (NHTSA), traffic fatality rate has been increased by 6% in 2012 and on an average nearly 4,743 pedestrians were killed which accounted for 14% of the total traffic related fatalities along with 76, 000 ended up injured in USA [1]
In our proposed work we have developed a pedestrian detection system by combining two features that is HOG and motion vectors
This work has presented a novel technique for underlying problem of pedestrian detection by incorporation of motion information with feature extraction technique of HOG
Summary
According to National Highway Traffic Safety Administration (NHTSA), traffic fatality rate has been increased by 6% in 2012 and on an average nearly 4,743 pedestrians were killed which accounted for 14% of the total traffic related fatalities along with 76, 000 ended up injured in USA [1]. In countries of Asia and Europe due to high population, the rate of road user deaths is much higher This rate of pedestrian deaths and injuries could be reduced by employing intelligent frameworks for detecting people on road. Safety components are intended to avoid the impacts and resulting casualties and injuries by offering advancements that alert the driver to potential issues before time. These safety advancements might lit up the car light, automatic braking, consolidate GPS traffic flow notifications, interface with cell phones, alert driver about other cars and dangers, keep the driver in the right lane, or show what is in blind corners. The essential target of road user detection based vision frameworks is to avoid impact of vehicles with people while driving
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