10,585 research outputs found
Data association and occlusion handling for vision-based people tracking by mobile robots
This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets
Pedestrian detection in uncontrolled environments using stereo and biometric information
A method for pedestrian detection from challenging real world outdoor scenes is presented in this paper. This technique is able to extract multiple pedestrians, of varying orientations and appearances, from a scene even when faced with large and multiple occlusions. The technique is also robust to changing background lighting conditions and effects, such as shadows. The technique applies an enhanced method from which reliable disparity information can be obtained even from untextured homogeneous areas within a scene. This is used in conjunction with ground plane estimation and biometric information,to obtain reliable pedestrian regions. These regions are robust to erroneous areas of disparity data and also to severe pedestrian occlusion, which often occurs in unconstrained scenarios
CoMaL Tracking: Tracking Points at the Object Boundaries
Traditional point tracking algorithms such as the KLT use local 2D
information aggregation for feature detection and tracking, due to which their
performance degrades at the object boundaries that separate multiple objects.
Recently, CoMaL Features have been proposed that handle such a case. However,
they proposed a simple tracking framework where the points are re-detected in
each frame and matched. This is inefficient and may also lose many points that
are not re-detected in the next frame. We propose a novel tracking algorithm to
accurately and efficiently track CoMaL points. For this, the level line segment
associated with the CoMaL points is matched to MSER segments in the next frame
using shape-based matching and the matches are further filtered using
texture-based matching. Experiments show improvements over a simple
re-detect-and-match framework as well as KLT in terms of speed/accuracy on
different real-world applications, especially at the object boundaries.Comment: 10 pages, 10 figures, to appear in 1st Joint BMTT-PETS Workshop on
Tracking and Surveillance, CVPR 201
Structured Light-Based 3D Reconstruction System for Plants.
Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance
Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features
Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong
performance on RGB salient object detection. Although, depth information can
help improve detection results, the exploration of CNNs for RGB-D salient
object detection remains limited. Here we propose a novel deep CNN architecture
for RGB-D salient object detection that exploits high-level, mid-level, and low
level features. Further, we present novel depth features that capture the ideas
of background enclosure and depth contrast that are suitable for a learned
approach. We show improved results compared to state-of-the-art RGB-D salient
object detection methods. We also show that the low-level and mid-level depth
features both contribute to improvements in the results. Especially, F-Score of
our method is 0.848 on RGBD1000 dataset, which is 10.7% better than the second
place
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