# Big Data and AI-Driven Product Design: A Survey
## 4 Product Design Based on Big Data and AI
### Product Design Based on Video Data  
Videos showcasing the usage of products are also valuable source of information for product design. They can be viewed repeatedly without restriction, making it easier to obtain hard-to-find but essential information. For instance, by watching the cooking videos of homemakers, [PERSON] found that standing up while taking food made them feel more comfortable than bending over. Additionally, [PERSON] found that the refrigerator compartment was used more frequently than the freezer compartment. Based on these insights, he improved the traditional refrigerator structure by changing the upper layer to a refrigerator compartment and the lower layer to a freezer compartment [231], which is still in use today. Although video data is useful for product design, manual viewing can be time-consuming, and is only suitable for small data volume scenes. In comparison, intelligent video analysis supports motion detection, video summarization, video retrieval, color detection, object detection, emotion recognition, and more [219; 232; 233; 234]. This capability makes it possible to apply big video data to user requirement acquisition, user behavior observation, experience improvement, and product virtual display. Intelligent video analysis establishes a mapping relationship between images and their descriptions, allowing computers to understand video through digital image analysis. _Product detection._ Compared to single image, object detection in the video has a temporal context, which helps to solve the redundancy between consecutive frames, motion blur, unfocused image, partial occlusion, singular postures, etc. [PERSON] et al. [235] proposed a novel method for annotating products in videos by identifying keyframes and extracting SIFT features to generate BOVW histograms, which were then compared with the visual signature of products for annotation. Meanwhile, [PERSON] et al. [236] developed a framework for identifying clothes worn by celebrities in videos, which involved utilizing DCNN for tasks such as human body detection, human posture selection, human pose estimation, face verification, and clothing detection. Additionally, [PERSON] et al. [237] linked clothes worn by stars with online shops to provide clothing recommendations. To improve the matching results, [PERSON] et al. [238] used image feature network (IFN) and video feature network (VFN) to generate deep visual features for shopping images and clothing trajectories in videos. _User behavior observation._ Understanding user behavior is crucial in designing products and improving user experience. To explore user behaviors in VR spherical video streaming, [PERSON] et al. [239] collected a head tracking dataset. Additionally, [PERSON] et al. [240] evaluated product usability through video analysis. They performed temporal segmentation of video featuring human-product interaction, automatically identifying time segments where users encountered difficulties. They took water faucet design as an example and used optical flow for motion detection. Optical flow, temporal difference, and background subtraction are commonly used for motion detection in videos [241]. _Product virtual display._ Compared to images, video-based virtual displays demand higher spatiotemporal consistency, but they offer a more comprehensive display for customers. Various AI algorithms have been proposed to enhance the customer experience. For instance, [PERSON] et al. [242] presented a parsing model to predict human poses in the video, while [PERSON] et al. [243] proposed a flow-navigated warping GAN (FW-GAN) to generate try-on videos conditioned on person, clothing images, and a series of target poses. Interactive displays not only promote product understanding but also evoke enjoyment during the experience [244]. To this end, [PERSON] et al. [245] designed a video composition system that displays products on mobile phones in an interactive and 3D-like manner.