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Generalized Haar Filter-Based Object Detection for Car Sharing Services
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DOI：10.1109/TASE.2018.2830655.png)
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摘要
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Object detection is important in car sharing services. Accuracy, efficiency, and low memory consumption are desirable for object detection in car sharing services. This paper presents a network system that satisfies all these requirements. Our approach first divides the object detection task into multiple simpler local regression tasks. Then, we propose the generalized Haar filter-based convolutional neural network to reduce the consumption of memory and computing resource. To achieve real-time performance, we introduce a sparse window generation strategy to reduce the number of input image patches without sacrificing accuracy. We perform experiments on both vehicle and pedestrian data sets. Experimental results demonstrate that our approach can accurately detect objects under challenging conditions.
Keyword:
Car sharing
convolutional neural network (CNN)
Haar filter
object detection
traffic scene
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