Pedestrian detection method, device and equipment, and computer readable storage medium

Abstract

The invention discloses a pedestrian detection method. The method comprises the following steps: extracting a shot pedestrian image to obtain a visible part boundary block diagram; inputting the pedestrian image into a detector, inputting the output of the Nth detection layer in the detector and the visible part boundary block diagram into a semantic segmentation model, and outputting a semantic attention diagram and semantic segmentation loss by the semantic segmentation model; inputting the semantic attention map into the (N + 1) th detection layer of the detector, and outputting a prediction boundary frame diagram and detection loss by the detector; Calculating the sum of semantic segmentation loss and detection loss to obtain total loss; if the total loss is greater than a preset lossthreshold, adjusting internal adjustable parameters of the detector and the semantic segmentation model according to a preset rule, and repeating the detection operation; otherwise, outputting and displaying the prediction boundary block diagram. According to the method, missing detection or false detection caused by inter-class shielding is reduced through the semantic segmentation model, the pedestrian detection accuracy is ensured, the number of training objects is small, and the cost is low. The invention further discloses a device and equipment based on the method and a computer readablestorage medium.

Description

technical field [0001] The present invention relates to the technical field of pedestrian detection, in particular to a pedestrian detection method, device, equipment and computer-readable storage medium. Background technique [0002] Pedestrian detection is an important research topic in object detection and has very high value for many applications, such as driverless cars, intelligent robots and intelligent transportation. It is common to directly detect pedestrians using methods proposed in Object Detection. However, it is difficult for these methods to achieve optimal performance. The main reason is that pedestrians often gather together and are easily blocked by other objects in reality. Therefore, it is challenging and meaningful to deal with the occlusion problem in pedestrian detection. In the context of pedestrian occlusion detection, missed and false detections often occur when detecting occluded pedestrians because the detector learns features from pedestrian ...

AI Technical Summary

Problems solved by technology

In the previous work, building pedestrian templates is the mainstream of pedestrian detection. The template method divides pedestrians into different parts as templates, and then uses these templates to train different classifiers to detect various occlusions. However, this method requires multiple classifications. device, the calculation cost is high and the calculation process is complicated

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