Video Analysis and Rule-Based Reasoning for Driving Maneuver Classification at Intersections

Video Analysis and Rule-Based Reasoning for Driving Maneuver Classification at Intersections

Blog Article

We propose a system for monitoring the driving maneuver at road intersections using rule-based reasoning and deep learning-based computer vision techniques.Along with detecting and classifying turning movements online, the system also detects violations such as ignoring STOP signs and failing to yield the right-of-way to other drivers.There is no distinction between temporarily and permanently stopped vehicles in the majority of frameworks proposed in the literature.Therefore, to color touch 7/97 conduct an accurate right-of-way study, permanently stopped vehicles should be excluded not to confound the results.Moreover, we also propose in this work a low-cost Convolutional Neural Network (CNN)-based object detection framework able to detect moving and temporally stopped vehicles.

The detection framework combines the reasoning system with background subtraction and a CNN-based object detector.The obtained results are promising.Compared to the conventional CNN-based methods, the detection framework reduces the execution time of the object detection module by about 30% (i.e., 54.

1 instead of 75ms/image) while preserving the same detection reliability.The accuracy of trajectory recognition is 95.32%, that of the zero-speed detection is 96.67%, 100w products and the right-of-way detection was perfect.