摘要 :
By using saccadic motion, human beings perform visual perception. In this paper, a function for object detection using saccadic motion is proposed. To measure distance, the proposed object detection function is built into a system...
展开
By using saccadic motion, human beings perform visual perception. In this paper, a function for object detection using saccadic motion is proposed. To measure distance, the proposed object detection function is built into a system with stereo vision. Saccadic motion is very important in maintaining human visual perception. In this paper, we propose a function for detecting objects based on the saccadic motion of human eyes. Experimental results show the ability of this function.
收起
摘要 :
This paper proposes a novel method of abandoned object detection and removed object detection. The statistical model is used to establish short-term background and long-term background. Background differencing and coterminous fram...
展开
This paper proposes a novel method of abandoned object detection and removed object detection. The statistical model is used to establish short-term background and long-term background. Background differencing and coterminous frame differencing is combined to obtain the moving object, and only the non-moving region of short-term background is updated in real-time. Differencing the short-term background and long-term background each 10 frames to obtain the object region, and the edge determinant method and the Bhattacharya coefficient determinant method is combined to determine whether the object is abandoned or removed. The experiments testify that proposed method detects abandoned and removed objects accurately.
收起
摘要 :
The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection. We propose a novel method for generating object bounding box proposals using edges. Edges provide a spars...
展开
The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection. We propose a novel method for generating object bounding box proposals using edges. Edges provide a sparse yet informative representation of an image. Our main observation is that the number of contours that are wholly contained in a bounding box is indicative of the likelihood of the box containing an object. We propose a simple box objectness score that measures the number of edges that exist in the box minus those that are members of contours that overlap the box's boundary. Using efficient data structures, millions of candidate boxes can be evaluated in a fraction of a second, returning a ranked set of a few thousand top-scoring proposals. Using standard metrics, we show results that are significantly more accurate than the current state-of-the-art while being faster to compute. In particular, given just 1000 proposals we achieve over 96% object recall at overlap threshold of 0.5 and over 75% recall at the more challenging overlap of 0.7. Our approach runs in 0.25 seconds and we additionally demonstrate a near real-time variant with only minor loss in accuracy.
收起
摘要 :
The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection. We propose a novel method for generating object bounding box proposals using edges. Edges provide a spars...
展开
The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection. We propose a novel method for generating object bounding box proposals using edges. Edges provide a sparse yet informative representation of an image. Our main observation is that the number of contours that are wholly contained in a bounding box is indicative of the likelihood of the box containing an object. We propose a simple box objectness score that measures the number of edges that exist in the box minus those that are members of contours that overlap the box's boundary. Using efficient data structures, millions of candidate boxes can be evaluated in a fraction of a second, returning a ranked set of a few thousand top-scoring proposals. Using standard metrics, we show results that are significantly more accurate than the current state-of-the-art while being faster to compute. In particular, given just 1000 proposals we achieve over 96% object recall at overlap threshold of 0.5 and over 75% recall at the more challenging overlap of 0.7. Our approach runs in 0.25 seconds and we additionally demonstrate a near real-time variant with only minor loss in accuracy.
收起
摘要 :
The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection. We propose a novel method for generating object bounding box proposals using edges. Edges provide a spars...
展开
The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection. We propose a novel method for generating object bounding box proposals using edges. Edges provide a sparse yet informative representation of an image. Our main observation is that the number of contours that are wholly contained in a bounding box is indicative of the likelihood of the box containing an object. We propose a simple box objectness score that measures the number of edges that exist in the box minus those that are members of contours that overlap the box's boundary. Using efficient data structures, millions of candidate boxes can be evaluated in a fraction of a second, returning a ranked set of a few thousand top-scoring proposals. Using standard metrics, we show results that are significantly more accurate than the current state-of-the-art while being faster to compute. In particular, given just 1000 proposals we achieve over 96% object recall at overlap threshold of 0.5 and over 75% recall at the more challenging overlap of 0.7. Our approach runs in 0.25 seconds and we additionally demonstrate a near real-time variant with only minor loss in accuracy.
收起
摘要 :
This work contributes to part-based object detection and recognition by introducing an enhanced method for local part detection. The method is based on complex-valued multiresolution Gabor features and their ranking using multiple...
展开
This work contributes to part-based object detection and recognition by introducing an enhanced method for local part detection. The method is based on complex-valued multiresolution Gabor features and their ranking using multiple hypothesis testing. In the present work, our main contribution is the introduction of a canonical object space, where objects are represented in their ``expected pose and visual appearance''. The canonical space circumvents the problem of geometric image normalisation prior to feature extraction. In addition, we define a compact set of Gabor filter parameters, from where the optimal values can be easily devised. These enhancements make our method an attractive landmark detector for part-based object detection and recognition methods.
收起
摘要 :
This work contributes to part-based object detection and recognition by introducing an enhanced method for local part detection. The method is based on complex-valued multiresolution Gabor features and their ranking using multiple...
展开
This work contributes to part-based object detection and recognition by introducing an enhanced method for local part detection. The method is based on complex-valued multiresolution Gabor features and their ranking using multiple hypothesis testing. In the present work, our main contribution is the introduction of a canonical object space, where objects are represented in their ``expected pose and visual appearance''. The canonical space circumvents the problem of geometric image normalisation prior to feature extraction. In addition, we define a compact set of Gabor filter parameters, from where the optimal values can be easily devised. These enhancements make our method an attractive landmark detector for part-based object detection and recognition methods.
收起
摘要 :
Many works address the problem of object detection by means of machine learning with boosted classifiers, They exploit sliding window search, spanning the whole image: the patches, at all possible positions and sizes, are sent to ...
展开
Many works address the problem of object detection by means of machine learning with boosted classifiers, They exploit sliding window search, spanning the whole image: the patches, at all possible positions and sizes, are sent to the classifier. Several methods have been proposed to speed up the search (adding complementary features or using specialized hardware). In this paper we propose a statistical-based search approach for object detection which uses a Monte Carlo sampling approach for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multi-stage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifier (i.e. its response). For videos, this approach is plugged in a Bayesian-recursive framework which exploits the temporal coherency of the pedestrians. Several tests on both still images and videos on common datasets are provided in order to demonstrate the relevant speedup and the increased localization accuracy with respect to sliding window strategy using a pedestrian classifier based on covariance descriptors and a cascade of Logitboost classifiers.
收起
摘要 :
Many works address the problem of object detection by means of machine learning with boosted classifiers. They exploit sliding window search, spanning the whole image: the patches, at all possible positions and sizes, are sent to ...
展开
Many works address the problem of object detection by means of machine learning with boosted classifiers. They exploit sliding window search, spanning the whole image: the patches, at all possible positions and sizes, are sent to the classifier. Several methods have been proposed to speed up the search (adding complementary features or using specialized hardware). In this paper we propose a statistical-based search approach for object detection which uses a Monte Carlo sampling approach for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multi-stage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifier (i.e. its response). For videos, this approach is plugged in a Bayesian-recursive framework which exploits the temporal coherency of the pedestrians. Several tests on both still images and videos on common datasets are provided in order to demonstrate the relevant speedup and the increased localization accuracy with respect to sliding window strategy using a pedestrian classifier based on covariance descriptors and a cascade of Logitboost classifiers.
收起
摘要 :
Object detection has always been one of the hot tasks in the computer vision community, whose goal is to locate the instances from the image and predict instances' category. In recent years, with the development of deep learning t...
展开
Object detection has always been one of the hot tasks in the computer vision community, whose goal is to locate the instances from the image and predict instances' category. In recent years, with the development of deep learning technology, both the accuracy and speed of object detection have made great progress. However, limited by the low resolution and little feature information of the small objects, detecting the small object is still facing many difficulties and attracting more and more researchers' attention. In this paper, we first introduce the mainstream object detection algorithms, and then detail the development of small object detection algorithms from the perspective of the data enhancement, context learning, adversarial learning, feature fusion, and other aspects. Also, we analyze the performance of these representative algorithms on the common datasets. Finally, we summarize the existing problems and prospect the possible future development direction in the small object detection research field.
收起
原文获取