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In 3D object recognition, local feature-based recognition is known to be robust against occlusion and clutter. Local feature estimation requires feature correspondences, including feature extraction and matching. Feature extraction is normally a two-stage process that estimates keypoints and keypoint descriptors, and existing studies show repeatability to be a good indicator of keypoint feature detector...
Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive...
Fractal analysis has been widely used in computer vision, especially in texture image processing and texture analysis. The key concept of fractal-based image model is the fractal dimension, which is invariant to bi-Lipschitz transformation of image, and thus capable of representing intrinsic structural information of image robustly. However, the invariance of fractal dimension generally does not hold...
To enable an intelligent traffic light system (ITLS) to consider the interactions between the signal controls and the traffic flow distribution resulting from the selfish-routing behaviors of travelers, a dynamic origin-destination (O-D) demand estimation model and a dynamic combined traffic assignment and signal control (CTA-SC) model are needed. However, the ITLS may collect inaccurate and incomplete...
Visual tracking integrates the technology of image processing and pattern recognition, etc., which has a lot of potential applications, such as automatic driving, safety monitoring, etc. This paper analyzes the advantages and disadvantages of the Kernelized Correlation Filter (KCF) and Tracking-Learning-Detection (TLD), which are two kinds of trackers. TLD tracker has correcting capability whereas...
In this paper, we address the problem of visual tracking in videos without using a pre-learned model of the object. This type of model-free tracking is a hard problem because of limited information about the object, abrupt object motion, and shape deformation. We propose to integrate an object-agnostic prior, called objectness, which is designed to measure the likelihood of a given location to contain...
Depth information improves skeleton detection, thus skeleton based methods are the most popular methods in RGB-D action recognition. But skeleton detection working range is limited in terms of distance and view-point. Most of the skeleton based action recognition methods ignore fact that skeleton may be missing. Local points-of-interest (POIs) do not require skeleton detection. But they fail if they...
Computer forensics is the crucial technology against computer crimes. However, existing forensics methods or technology are inefficient and their stringencies are poor. This paper proposed a novel dynamic computer forensics model (DAIP) based on artificial immune and real-time network fatalness, which can vivify the crime scene. The definitions of self, non-self, and immunocyte in the network transactions...
We discuss the properties of a class of latent variable models that assumes each labeled sample is associated with a set of different features, with no prior knowledge of which feature is the most relevant feature to be used. Deformable-Part Models (DPM) can be seen as good examples of such models. These models are usually considered to be expensive to train and very sensitive to the initialization...
We propose a novel keypoint-based method for long-term model-free object tracking in a combined matching-and-tracking framework. In order to localise the object in every frame, each keypoint casts votes for the object center. As erroneous keypoints are hard to avoid, we employ a novel consensus-based scheme for outlier detection in the voting behaviour. To make this approach computationally feasible,...
In this paper, we propose a novel approach based on online learning for accurate and effective detection of abandoned objects. Most existing methods for abandoned objects detection only detect abandoned objects without considering of the logic owner of the abandoned object. These methods need an advanced trained human detector to discriminate abandoned objects from still persons frequently. However,...
In this paper we introduce a new person tracking-by-detection approach based on a particle filter. We leverage detection and appearance cues and apply explicit occlusion reasoning. The approach samples efficiently from a large set of available person part-detectors in order to increase runtime performance while retaining accuracy. The tracking approach is evaluated and compared to the state of the...
This paper focuses on image registration between sequential frames in aerial infrared video. To address this problem, we propose an efficient approach based on an improved Smallest Univalue Segment Assimilating Nucleus (SUSAN) corner detector and a novel corner rejection strategy. Firstly, the SUSAN corners are detected via the concatenation of enlarging the radius of original detector and down-sampling...
This paper presents an in-depth analysis of the SIFT and SURF feature detection and matching techniques in characterizing natural environments for vision based navigation problems, in particular, the performance of feature extraction algorithms and matching when both visual and infrared data are used. With successful utilization of both feature extraction methods on different characteristic images,...
The main objective in content-based image retrieval is to find images similar to a query image in an image collection. Matching using descriptors computed from regions centered at local invariant interest points (key points) have become popular because of their robustness to changes in viewpoint and occlusion. However, local descriptor matching can produce many false matches. RANSAC can robustly fit...
In detection theory, the optimal Neyman-Pearson rule applies when the characteristics of the signal and the noise are completely known. However, in many practical scenarios such as multipath or moving targets, only partial knowledge of the signal can be obtained. In this paper, we examine the case when the alternative hypothesis has multiple candidate models, and apply the multimodal sensor integration...
Hand posture recognition (HPR) plays an important role in human-computer interaction (HCI) since it is one of the most common and natural ways of communication among human beings. Different fingers often represent different meanings which will attract more attentions in HPR research. Based on finger geometric feature and its classification, we develop a HPR system that can tell its posture on possible...
This paper presents a novel approach to locate corresponding regions between two views in urban environments despite the presence of repetitive structures and widely separated views. First we extract hypotheses of building facades, each defined by a rectangular region. The inputs from each pair of regions in two images derive a projective transformation model. Extracted lines and points are used to...
This paper presents a saliency-based solution to boost trail detection. The proposed model builds on the empirical observation that trails are usually conspicuous structures in natural environments. This hypothesis is confirmed by the experimental results, where a strong positive correlation between trail location and visual saliency has been observed. These results are due in part to the proposed...
Stereo-based off-road obstacle detection is a complex and still open problem. The challenges are in great extent related to computational cost and noise level. Previous work has shown that visual saliency and voting mechanisms are extremely effective in tackling these issues. This paper proposes a set of extensions to these mechanisms, to further improve the detector's speed-accuracy trade-off as...
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