The Marketable Foods (MF) dataset was originally constructed to fine-tune the language and visual network layers and facilitates backdoor injections in text-to-image generative models. The dataset consists of images from three popular food corporations with prominent, recognisable brands (Coffee = Starbucks, Burger = McDonald's, Drink = Coca Cola). Samples were collected from the internet and were cleaned using a filtering algorithm discussed in the corresponding paper.

This data package（.zip） includes data of follower robots'  motion track、errors  and velocities in five simulatd experimental cases：(1),(2): set obstacle range on 0.8m and 1.0m，2 groups；(3):  oneside situation,  and the number of follower robots rises to 5.   (4)：complex environment, which we place more obstacles.   （5): change the lead-follower formation  （6),(7)：two types of formation tracks, circle and straight line，compare follower1，2.

The extraction and construction of high-precision and long-distance vehicle trajectory data and microscopic traffic flow characteristics are critical for traffic safety studies. Current research typically relies on a limited number of datasets which suffer from vehicle detection inaccuracy and limitation of the coverage area. Therefore, we establish a high-precision and long-distance vehicle trajectory dataset of urban scenarios, which is also named as WUT-NGSIM.

In this paper, we propose a framework for 3D human pose estimation using a single 360° camera mounted on the user's wrist. Perceiving a 3D human pose with such a simple setup has remarkable potential for various applications (e.g., daily-living activity monitoring, motion analysis for sports training). However, no existing method has tackled this task due to the difficulty of estimating a human pose from a single camera image in which only a part of the human body is captured, and because of a lack of training data.