Real-time Object Detection of Retail Products for Eye Tracking
Fang Wu, Kezheng Zhang · 2020
Object detection is one important task in automatically analyzing eye tracking video data. This paper presents a real-time object detection method of retail products based on deep learning for eye tracking system. In the proposed approach, an eye tracking based Convolutional Neural Networks is constructed to obtain the feature of original images. Then, a weighted bounding box selection strategy based on gaze location is used for object detection. Besides, parameters are adjusted according to gaze location of eye tracking. Experimental results show that our method can achieve better accuracy for eye tracking than other existing methods in the detection of retail products.