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Conservation of wild animals needs adaptive management which is a systematic process for continually improving management policies and practices by learning from monitoring results. However, there is not enough the population size information of large-sized mammals because of their large habitat area. Thus, it is expected to estimate population densities of large-sized mammals using remote sensing...
Numerous efforts have been made to detect salient regions in images. Mostly luminance-based saliency models are found in the literature, which ignore the important contribution of color in finding the local distinct image features. Methods about color saliency detection in the literature can only give indication of color salient points or derivatives. In this paper, we present a fast method for detecting...
In this paper, we describe a smart surveillance system to detect human faces in stereo images with applications to advanced video surveillance systems. The system utilizes two smart IP cameras to obtain the position and location of the object that is a human face. The position and location of the object are extracted from two IP cameras and subsequently transmitted to a Pan-Tilt-Zoom (PTZ) camera,...
Many service robots and various service modules are being developed. Robots for services, such as guiding, escorting, and nursing, require human-friendly navigation, which is an appropriate objective of interaction. This paper proposes a method for a mobile robot to detect a human leg and to follow the human for interaction with him/her. Most human-tracking schemes in mobile robots utilize vision...
In this paper, we present a visual scene description and interaction framework for pedestrian and mobile objects detection and tracking applications. The framework is built upon a previously developed stereo vision system. The proposed algorithms raise up the information level in order to allow to query about the scene using natural language or semantic operators and give a simpler interface with...
Human detection and recognition at a distance is recently a matter of great concern among computer vision researchers. This paper introduces a new set of human body features for the recognition of detected human as an object. The feature extraction is performed by an established human model consisting of five parts. These features consist of geometric calculations of detected object and their different...
In this paper, image processing techniques are applied to the analysis of near-infrared videos. The goal is to detect human activities in the videos. For detecting human activities, we implement the Gaussian mixture modeling (GMM) to construct background model and to perform foreground detection. Additionally, we pay attention to commonly use sensing lighting equipments used in nighttime environment...
This paper presents a novel control approach based on digital image processing for elevator door protection system. This system is a non-contact type of elevator door protection system, which incorporates the background difference method and inter-frame difference method to achieve moving object detection and identifies the human body by the ratio of the image area and domain of variation. Finally,...
In this paper, we describe a system for smart surveillance using stereo images with applications to advanced video surveillance systems. The system utilizes two smart IP cameras to obtain the position and location of objects. In this case, the object target is human face. The position and location of the object are automatically extracted from two IP cameras and subsequently transmitted to an ACTi...
We present a method to classify and localize human actions in video using a Hough transform voting framework. Random trees are trained to learn a mapping between densely-sampled feature patches and their corresponding votes in a spatio-temporal-action Hough space. The leaves of the trees form a discriminative multi-class codebook that share features between the action classes and vote for action centers...
Object detection for computer vision is one of the key factors for scene understanding. It is still a challenge today to accurately determine an object from a background where similar shaped objects are present in a large number. In this paper we proposed a method for object detection from such chaotic background by using image segmentation and graph partitioning. We build a “feature set” from the...
This paper describes an algorithm enabling a human supervisor to convey task-level information to a robot by using stylus gestures to circle one or more objects within the field of view of a robot-mounted camera. These gestures serve to segment the unknown objects from the environment. Our method's main novelty lies in its use of appearance-based object “reacquisition” to reconstitute the supervisory...
Blind people face a number of challenges when interacting with their environments because so much information is encoded visually. Text is pervasively used to label objects, colors carry special significance, and items can easily become lost in surroundings that cannot be quickly scanned. Many tools seek to help blind people solve these problems by enabling them to query for additional information,...
This paper presents an algorithm for recovering the globally optimal 2D human figure detection using a loopy graph model. This is computationally challenging because the time complexity scales exponentially in the size of the largest clique in the graph. The proposed algorithm uses Branch and Bound (BB) to search for the globally optimal solution. The algorithm converges rapidly in practice and this...
While the problem of tracking 3D human motion has been widely studied, most approaches have assumed that the person is isolated and not interacting with the environment. Environmental constraints, however, can greatly constrain and simplify the tracking problem. The most studied constraints involve gravity and contact with the ground plane. We go further to consider interaction with objects in the...
Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation...
Detecting objects in cluttered scenes and estimating articulated human body parts are two challenging problems in computer vision. The difficulty is particularly pronounced in activities involving human-object interactions (e.g. playing tennis), where the relevant object tends to be small or only partially visible, and the human body parts are often self-occluded. We observe, however, that objects...
Current computer science has paid more and more attention to the human being and the interaction between human being and computer. Eye-gaze is an important communication cue of human being. The better understanding of the underlying mechanisms of eye-gaze can facilitate development of many computer research fields. Previous fundamental researches about eye-gaze have proved that an uninformative cue...
This paper proposes a new learning method, which integrates feature selection with classifier construction for human detection via solving three optimization models. Firstly, the method trains a series of weak-classifiers by the proposed L1-norm Minimization Learning (LML) and min-max penalty function models. Secondly, the proposed method selects the weak-classifiers by using the integer optimization...
We propose a novel method for automatic camera calibration and foot-head homology estimation by observing persons standing at several positions in the camera field of view. We demonstrate that human body can be considered as a calibration target thus avoiding special calibration objects or manually established fiducial points. First, by assuming roughly parallel human poses we derive a new constraint...
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