用深度学习从监控视频中识别顾客年龄性别表情，助力零售营销。
Deep Learning based approach to detect Customer Age, Gender and Expression in Surveillance Video
- 结合Wide ResNet与Xception模型，直接处理低清带噪监控视频。
- 在真实服装店场景下实现高精度年龄性别预测，支持遮挡光照变化。
- 额外检测购买时表情，为个性化营销提供数据支持。
在当前信息时代，客户分析对商业成功至关重要。由于客户人口统计特征主要决定其偏好，利用年龄与性别信息进行销售预测，可最大化零售收益。本文提出一种基于计算机视觉的监控视频年龄与性别预测方法。该方法采用Wide Residual Networks和Xception深度学习模型，直接处理典型CCTV系统采集的原始视频。在真实服装店监控视频上评估了该方法的有效性，视频由低分辨率摄像头拍摄，存在非均匀光照、人群遮挡及环境噪声等问题。系统还能检测客户在购买过程中的面部表情，可用于制定有效营销策略，提升客户群体销售额。
原文摘要 · Abstract (English)
In the current information era, customer analytics play a key role in the success of any business. Since customer demographics primarily dictate their preferences, identification and utilization of age & gender information of customers in sales forecasting, may maximize retail sales. In this work, we propose a computer vision based approach to age and gender prediction in surveillance video. The proposed approach leverage the effectiveness of Wide Residual Networks and Xception deep learning models to predict age and gender demographics of the consumers. The proposed approach is designed to work with raw video captured in a typical CCTV video surveillance system. The effectiveness of the proposed approach is evaluated on real-life garment store surveillance video, which is captured by low resolution camera, under non-uniform illumination, with occlusions due to crowding, and environmental noise. The system can also detect customer facial expressions during purchase in addition to demographics, that can be utilized to devise effective marketing strategies for their customer base, to maximize sales.
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