Generalized Haar Filter-Based Object Detection for Car Sharing Services
| Autor: | Hangen He, Jian Li, Xiping Hu, Keyu Lu, Li Zhou, Xiangjing An |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Deep learning Real-time computing Feature extraction Intelligent vehicle technologies 02 engineering and technology Convolutional neural network Object detection 020901 industrial engineering & automation Resource (project management) Memory management Control and Systems Engineering Filter (video) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | IEEE Transactions on Automation Science and Engineering. 15:1448-1458 |
| ISSN: | 1558-3783 1545-5955 |
| DOI: | 10.1109/tase.2018.2830655 |
| Popis: | 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. Note to Practitioners —Object detection is an important part of intelligent vehicle technologies, which play an important role in car sharing services. Object detection provides metadata for collision avoidance, self-driving systems, and driver-assistance systems, which can result in better safety and consumer experiences in car sharing services. Although deep learning has achieved an excellent performance in object detection, they consume a large amount of storage and computing resource, which makes them difficult to be deployed for car sharing services. This paper suggests a novel approach which is based on the generalized Haar filter and the local regression strategy. Our approach is accurate, efficient, and light. The experimental results verify the effectiveness of the proposed approach in car sharing services. |
| Databáze: | OpenAIRE |
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