Optimized HOG for on-road video based vehicle verification

Vision-based object detection from a moving platform becomes particularly challenging in the field of advanced driver assistance systems (ADAS). In this context, onboard vision-based vehicle verification strategies become critical, facing challenges derived from the variability of vehicles appearance, illumination, and vehicle speed. In this paper, an optimized HOG configuration for onboard vehicle verification is proposed which not only considers its spatial and orientation resolution, but descriptor processing strategies and classification. An in-depth analysis of the optimal settings for HOG for onboard vehicle verification is presented, in the context of SVM classification with different kernels. In contrast to many existing approaches, the evaluation is realized in a public and heterogeneous database of vehicle and non-vehicle images in different areas of the road, rendering excellent verification rates that outperform other similar approaches in the literature

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.Comment: arXiv admin note: substantial text overlap with arXiv:1304.126

Efficient Pedestrian Detection in Urban Traffic Scenes

Pedestrians are important participants in urban traffic environments, and thus act as an interesting category of objects for autonomous cars. Automatic pedestrian detection is an essential task for protecting pedestrians from collision. In this thesis, we investigate and develop novel approaches by interpreting spatial and temporal characteristics of pedestrians, in three different aspects: shape, cognition and motion. The special up-right human body shape, especially the geometry of the head and shoulder area, is the most discriminative characteristic for pedestrians from other object categories. Inspired by the success of Haar-like features for detecting human faces, which also exhibit a uniform shape structure, we propose to design particular Haar-like features for pedestrians. Tailored to a pre-defined statistical pedestrian shape model, Haar-like templates with multiple modalities are designed to describe local difference of the shape structure. Cognition theories aim to explain how human visual systems process input visual signals in an accurate and fast way. By emulating the center-surround mechanism in human visual systems, we design multi-channel, multi-direction and multi-scale contrast features, and boost them to respond to the appearance of pedestrians. In this way, our detector is considered as a top-down saliency system. In the last part of this thesis, we exploit the temporal characteristics for moving pedestrians and then employ motion information for feature design, as well as for regions of interest (ROIs) selection. Motion segmentation on optical flow fields enables us to select those blobs most probably containing moving pedestrians; a combination of Histogram of Oriented Gradients (HOG) and motion self difference features further enables robust detection. We test our three approaches on image and video data captured in urban traffic scenes, which are rather challenging due to dynamic and complex backgrounds. The achieved results demonstrate that our approaches reach and surpass state-of-the-art performance, and can also be employed for other applications, such as indoor robotics or public surveillance. In this thesis, we investigate and develop novel approaches by interpreting spatial and temporal characteristics of pedestrians, in three different aspects: shape, cognition and motion. The special up-right human body shape, especially the geometry of the head and shoulder area, is the most discriminative characteristic for pedestrians from other object categories. Inspired by the success of Haar-like features for detecting human faces, which also exhibit a uniform shape structure, we propose to design particular Haar-like features for pedestrians. Tailored to a pre-defined statistical pedestrian shape model, Haar-like templates with multiple modalities are designed to describe local difference of the shape structure. Cognition theories aim to explain how human visual systems process input visual signals in an accurate and fast way. By emulating the center-surround mechanism in human visual systems, we design multi-channel, multi-direction and multi-scale contrast features, and boost them to respond to the appearance of pedestrians. In this way, our detector is considered as a top-down saliency system. In the last part of this thesis, we exploit the temporal characteristics for moving pedestrians and then employ motion information for feature design, as well as for regions of interest (ROIs) selection. Motion segmentation on optical flow fields enables us to select those blobs most probably containing moving pedestrians; a combination of Histogram of Oriented Gradients (HOG) and motion self difference features further enables robust detection. We test our three approaches on image and video data captured in urban traffic scenes, which are rather challenging due to dynamic and complex backgrounds. The achieved results demonstrate that our approaches reach and surpass state-of-the-art performance, and can also be employed for other applications, such as indoor robotics or public surveillance