Person re-identification has received much attention in the last few years, as it enhances the retrieval effectiveness in the video surveillance networks and video archive management. In this paper, we demonstrate a guiding robot with person followers system, which recognizes the follower using a person re-identification technology. It first adopts existing face recognition and person tracking methods to generate person tracklets with different IDs. Then, a classic person re-identification model, pre-trained on the surveillance dataset, is adapted to the new robot vision condition incrementally. The demonstration showcases the quality of robot follower focusing.

We present a novel method for detecting vehicles as obstacles in various road scenes using a single onboard camera. Vehicles are detected by testing whether the motion of a set of three horizontal line segments, which are always on the vehicles, satisfies the motion constraint of the ground plane or that of the surface plane of the vehicles. The motion constraint of each plane is derived from the projective invariant combined with the vanishing line of the plane that is a prior knowledge of road scenes. The proposed method is implemented into a newly developed onboard LSI. Experimental results for real road scenes under various conditions show the effectiveness of the proposed method.

We developed a robot system which can play one-on-one volleyball games with a human player. It can not only hit a flying ball back to the player correctly but also have various autonomous functionalities to interact with humans necessary for playing games, i.e., it can pick up a ball of a requested color by recognizing voice instructions, can locate the players by recognizing their faces, and can shake hands. This robot performed the entire game sequence over 100 times at a company-sponsored exhibition, where the ball rally continued over 25 times with an arbitrary player. In this paper, we mainly describe the real-time visual feedback system of the robot which is essential for playing ball games.

We propose a method for model- based scene segmentation which separates object instances from the background in the scene. An object model is composed of parameterized geo- metric primitives constrained to each other accord- ing to its shape and size variations, and the rela- tionships to other object classes. As sensory data, we use a pair of color stereo images which provides color and disparity information. Using these infor- mation and the constraints in model descriptions, we approach to the problems of scene segmenta- tion and object recognition. First, we construct a height map, which shows a top view of the input scene, transforming the disparity information into 3-D space based on the height and tilt of the camera system. Using the size constraints of the object model, we separate candidate areas for object instances from the back- ground. Next, parts of the model are extracted from the scene descriptions inside each candidate area, and free parameters in the object model are determined so that the instance can be best fitted to the model. Finally, uncertainty measure for pla- nar patches is defined and applied to the extracted parts. We show the preliminary results using car models.

An approach to geometrical reasoning for world model representations that are based on planar surfaces from range and video images is described. The geometrical reasoning is regarded as an inferring process of the spatial extent of primal surfaces derived from the range image at an early stage. The inferring process has two subprocesses: expansion of a primal surface using a directional uncertainty defined by the moments around the axes on the plane fitted to it, and the determination of the boundary shape of the expanded surface using constraints on the spatial relationships between the observed data. Experimental results that were applied to road scenes in which the inferring process proved useful for the integration process of video and range image sequences are discussed.>

Presents an approach to build a world model representation based on planar surfaces from range and video images. The method consists of three sub-processes: (1) to extract the primal surface patches from range and video images; (2) to define the directional uncertainty for the extracted surface patch; and (3) to match the model surfaces of the object expected in the scene with the extracted surface patches using the defined directional uncertainty, model constraints, and a certainty measure of matching, which is defined by directional uncertainty. The preliminary results applied to road scenes are shown.>