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Commonly, HoG/SVM classifier uses rectangular images for HoG feature descriptor extraction and training. This means significant additional work has to be done to process irrelevant pixels belonging to the background surrounding the object of interest. While some objects may indeed be square or rectangular, most of objects are not easily representable by simple geometric shapes. In Bitmap-HoG approach...
Real-world CCTV footage often poses increased challenges in object tracking due to Pan-Tilt-Zoom operations, low camera quality and diverse working environments. Most relevant challenges are moving background, motion blur and severe scale changes. Convolutional neural networks, which offer state-of-the-art performance in object detection, are increasingly utilized to pursue a more efficient tracking...
This paper deals with image categorization from weak supervision, e.g. global image labels. We propose to improve the region selection performed in latent variable models such as Latent Support Vector Machine (LSVM) by leveraging human eye movement features collected from an eye-tracker device. We introduce a new model, Gaze Latent Support Vector Machine (G-LSVM), whose region selection during training...
In this work, we present a new multiple channel feature called Deep Compact Channel Feature (DCCF), which generates a compact, discriminative feature representation by a pre-trained deep encoder-decoder. With the combination of DCCF and boosted decision trees, a new object detector is proposed which achieved outstanding performance on standard pedestrian dataset INRIA and Caltech. Furthermore, a large...
Intrinsic natures of different appearance between sub-regions of objects and non-objects in optical flows lead to more visual consistency for object proposals. Hence, visual variations in different sub-regions in video sequences over time is a good indicator for likeliness of objects. We propose a method that dynamically measures the objectness of each proposal by exploiting temporal consistency within...
Detecting eyes in images is fundamental for many computer vision applications including face detection, face recognition, and human-computer interaction. Most existing methods are designed and tested on datasets acquired under controlled lab settings (e.g., fixed scale, known poses, clean background, etc.), leaving their performance to be further examined on real-world, uncontrolled images, such as...
Deep neural networks yield positive object detection results in aerial imaging. To deal with the massive computational time required, we propose to connect an SVM Network to the different feature maps of a CNN. After the training of this SVM Network, we use an activation path to cross the network in a predefined order. We stop the crossing as quickly as possible. This early exit from the CNN allows...
In weakly supervised object detection, conventional methods treat object location in each image as a latent variable and use non-convex optimization to solve the latent variable. However, as the optimization objective is image-level instead of sample-level, the learning procedure tends to choose object parts as false positive samples. Furthermore, when multiple classes of objects appear in the same...
This paper tackles the problem of bird detection in large landscape images for applications in the wind energy industry. While significant progress in image recognition has been made by deep convolutional neural networks (CNNs), small object detection remains a problem. To solve it, we follow the idea that a detector can be tuned to small objects of interest and semantic segmentation methods can be...
In this paper we propose a multiple layer model for object detection and sketch representation. Unlike most traditional detection models focusing on the object localization, we investigate both the object detection and sketch representation within an unified framework. Based on the multiple layer architecture, our model can provide the sketch information of the detected object. Meanwhile, we generalize...
Passive Millimeter Wave Images (PMMWI) can be used to detect and localize objects concealed under clothing. Unfortunately, the quality of the acquired images and the unknown position, shape, and size of the hidden objects render difficult this task. In this paper we propose a method that combines image processing and statistical machine learning techniques to solve this localization/detection problem...
ShapeNets is an image representation, which is based on shape, compact structure, hierarchical image structure and appearance characteristic of object contour. In a ShapeNets, the shape of image is a window of containing objects which can be extracted with the method of objectness. The outline of objects can also be extracted in a line boundary detection algorithm based on histogram of gradients direction,...
Convolutional neural networks are a popular choice for current object detection and classification systems. Their performance improves constantly but for effective training, large, hand-labeled datasets are required. We address the problem of obtaining customized, yet large enough datasets for CNN training by synthesizing them in a virtual world, thus eliminating the need for tedious human interaction...
Traffic sign detection plays an important role in driving assistance systems and traffic safety. But the existing detection methods are usually limited to a predefined set of traffic signs. Therefore we propose a traffic sign detection algorithm based on deep Convolutional Neural Network (CNN) using Region Proposal Network(RPN) to detect all Chinese traffic sign. Firstly, a Chinese traffic sign dataset...
Object detection from still images has been among the most active and challenging area in computer vision recently. In contrast, fully supervised object detection from video has rarely been investigated. In this paper, we propose an algorithm to improve the performance of object detection from video. Our proposed method is based on an empirical property that the trajectory of an object is important...
Scene classification of high resolution remote sensing images plays an important role for a wide range of applications. While significant efforts have been made in developing various methods for scene classification, most of them are based on handcrafted or shallow learning-based features. In this paper, we investigate the use of deep convolutional neural network (CNN) for scene classification. To...
In recent years, unmanned aerial vehicles (UAVs) have been widely used for civilian remote sensing applications. One of them is to assess damages due to man-made or natural disasters and search for bodies in the debris. In this work, we propose to support avalanche search and rescue (SAR) operation with UAVs. The image acquired by the UAV is processed through a pre-trained convolutional neural network...
With rapid development of light detection and ranging (LiDAR) technologies, three dimensional point clouds increasingly become a new approach to sense the world. In our previous work, light poles were detected from mobile LiDAR point clouds without using their locations. In this paper, we improve our previous work by considering location information between two neighboring light poles to reduce false...
Object detection in very high resolution (VHR) optical remote sensing images is one of the most fundamental but challenging problems in the field of remote sensing image analysis. As object detection is usually carried out in feature space, effective feature representation is very important to construct a high-performance object detection system. During the last decades, a great deal of effort has...
This paper presents a block-based color cluster background modeling and a foreground detection algorithm that possesses efficient processing and low memory requirement in a complex scene. In training phase, the color cluster and pixel distribution line (PDL) are efficiently used to reduce the background information. In detection phase, we can extract the foreground objects precisely and fast in complex...
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