Real-Time Purchase Prediction Using Retail Video Analytics

The proliferation of video data in retail marketing brings opportunities for researchers to study customer behavior using rich video information. Our study demonstrates how to understand customer behavior of multiple dimensions using video analytics on a scalable basis. We obtained a unique video footage data collected from in-store cameras, resulting in approximately 20,000 customers involved and over 6,000 payments recorded. We extracted features on the demographics, appearance, emotion, and contextual dimensions of customer behavior from the video with state-of-the-art computer vision techniques and proposed a novel framework using machine learning and deep learning models to predict consumer purchase decision. Results showed that our framework makes accurate predictions which indicate the importance of incorporating emotional response into prediction. Our findings reveal multi-dimensional drivers of purchase decision and provide an implementable video analytics tool for marketers. It shows possibility of involving personalized recommendations that would potentially integrate our framework into omnichannel landscape

A fast multi-object tracking system using an object detector ensemble

Multiple-Object Tracking (MOT) is of crucial importance for applications such as retail video analytics and video surveillance. Object detectors are often the computational bottleneck of modern MOT systems, limiting their use for real-time applications. In this paper, we address this issue by leveraging on an ensemble of detectors, each running every f frames. We measured the performance of our system in the MOT16 benchmark. The proposed model surpassed other online entries of the MOT16 challenge in speed, while maintaining an acceptable accuracy.Comment: 5 pages, 4 figures, 1 table, published in 2019 IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI

Lost in Time: Temporal Analytics for Long-Term Video Surveillance

Video surveillance is a well researched area of study with substantial work done in the aspects of object detection, tracking and behavior analysis. With the abundance of video data captured over a long period of time, we can understand patterns in human behavior and scene dynamics through data-driven temporal analytics. In this work, we propose two schemes to perform descriptive and predictive analytics on long-term video surveillance data. We generate heatmap and footmap visualizations to describe spatially pooled trajectory patterns with respect to time and location. We also present two approaches for anomaly prediction at the day-level granularity: a trajectory-based statistical approach, and a time-series based approach. Experimentation with one year data from a single camera demonstrates the ability to uncover interesting insights about the scene and to predict anomalies reasonably well.Comment: To Appear in Springer LNE

Online Store Locator: An Essential Resource for Retailers in the 21st Century

Most retailers use their websites and social media to increase their visibility, while potential customers get information about these retailers using the Internet on electronic devices. Many papers have previously studied online marketing strategies used by retailers, but little attention has been paid to determine how these companies provide information through the Internet about the location and characteristics of their stores. This paper aims to obtain evidence about the inclusion of interactive web maps on retailers’ websites to provide information about the location of their stores. With this purpose, the store locator interactive tools of specialty retailers’ websites included in the report “Global Powers of Retailing 2015” are studied in detail using different procedures, such as frequency analysis and word clouds. From the results obtained, it was concluded that most of these firms use interactive maps to provide information about their offline stores, but today some of them still use non-interactive (static) maps or text format to present this information. Moreover, some differences were observed among the search filters used in the store locator services, according to the retailer’s specialty. These results provided insight into the important role of online store locator tools on retailers’ websites