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This paper presents fine-tuned CNN features for person re-identification. Recently, features extracted from top layers of pre-trained Convolutional Neural Network (CNN) on a large annotated dataset, e.g., ImageNet, have been proven to be strong off-the-shelf descriptors for various recognition tasks. However, large disparity among the pre-trained task, i.e., ImageNet classification, and the target...
Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification. In this paper, we present a novel descriptor based on a hierarchical distribution of pixel features. A hierarchical covariance descriptor has been successfully applied for image classification. However, the mean information of pixel features, which is absent in covariance,...
This paper proposes a novel localization approach based on visual impressions. We define a visual impression as the representation of a HSV color distribution of a place. The representation uses clustering feature (CF) tree to manage the color distribution and we propose to weight each CF entry to indicate its importance. The method compares the navigating tree, which is created by the robot from...
One of the effective methodologies for time series classification is to identify informative subsequence patterns in time series and exploit them as discriminative features. Previous studies on this methodology have achieved promising results using a small number of individually selected patterns. However, there remain difficulties in finding a set of related patterns or patterns of a minor class,...
In this paper we construct an office-use autonomous mobile robot which predicts the state (either stressed, relaxed, usual, or non-existent) of a person at different places and navigates between the places. The productivity of an office worker in advanced countries is a crucial concern and we believe autonomous mobile robots without network connection and with a privacy switch are preferred to privacy-offending...
Behavior analysis using trajectory data presents a practical and interesting challenge for KDD. Conventional analyses address discriminative tasks of behaviors, e.g., classification and clustering typically using the subsequences extracted from the trajectory of an object as a numerical feature representation. In this paper, we explore further to identify the difference in the high-level semantics...
This paper aims to develop a fast algorithm for detecting a new object from video sequences captured by on-board robot vision. We first propose lifting complex wavelet, which is a new method for extracting local features in an image. The proposed lifting complex wavelet transforms can be detected the features faster than the conventional SIFT algorithm. Our new object detection is performed by using...
In this paper we describe a Camshift implementation on mobile robotic system for tracking and pursuing a moving person with a monocular camera. Camshift algorithm uses color distribution information to track moving object. It is computationally efficient for working in real-time applications and robust to image noise. It can deal well with illumination changes, shadows and irregular objects motion...
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