HOG-Like gradient-based descriptor for visual vehicle detection

One of the main challenges for intelligent vehicles is the capability of detecting other vehicles in their environment, which constitute the main source of accidents. Specifically, many methods have been proposed in the literature for video-based vehicle detection. Most of them perform supervised classification using some appearance-related feature, in particular, symmetry has been extensively utilized. However, an in-depth analysis of the classification power of this feature is missing. As a first contribution of this paper, a thorough study of the classification performance of symmetry is presented within a Bayesian decision framework. This study reveals that the performance of symmetry-based classification is very limited. Therefore, as a second contribution, a new gradient-based descriptor is proposed for vehicle detection. This descriptor exploits the known rectangular structure of vehicle rears within a Histogram of Gradients (HOG)-based framework. Experiments show that the proposed descriptor outperforms largely symmetry as a feature for vehicle verification, achieving classification rates over 90%

Exploring Human Vision Driven Features for Pedestrian Detection

Motivated by the center-surround mechanism in the human visual attention system, we propose to use average contrast maps for the challenge of pedestrian detection in street scenes due to the observation that pedestrians indeed exhibit discriminative contrast texture. Our main contributions are first to design a local, statistical multi-channel descriptorin order to incorporate both color and gradient information. Second, we introduce a multi-direction and multi-scale contrast scheme based on grid-cells in order to integrate expressive local variations. Contributing to the issue of selecting most discriminative features for assessing and classification, we perform extensive comparisons w.r.t. statistical descriptors, contrast measurements, and scale structures. This way, we obtain reasonable results under various configurations. Empirical findings from applying our optimized detector on the INRIA and Caltech pedestrian datasets show that our features yield state-of-the-art performance in pedestrian detection.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

Vehicle make and model recognition using bag of expressions

This article belongs to the Section Intelligent SensorsVehicle make and model recognition (VMMR) is a key task for automated vehicular surveillance (AVS) and various intelligent transport system (ITS) applications. In this paper, we propose and study the suitability of the bag of expressions (BoE) approach for VMMR-based applications. The method includes neighborhood information in addition to visual words. BoE improves the existing power of a bag of words (BOW) approach, including occlusion handling, scale invariance and view independence. The proposed approach extracts features using a combination of different keypoint detectors and a Histogram of Oriented Gradients (HOG) descriptor. An optimized dictionary of expressions is formed using visual words acquired through k-means clustering. The histogram of expressions is created by computing the occurrences of each expression in the image. For classification, multiclass linear support vector machines (SVM) are trained over the BoE-based features representation. The approach has been evaluated by applying cross-validation tests on the publicly available National Taiwan Ocean University-Make and Model Recognition (NTOU-MMR) dataset, and experimental results show that it outperforms recent approaches for VMMR. With multiclass linear SVM classification, promising average accuracy and processing speed are obtained using a combination of keypoint detectors with HOG-based BoE description, making it applicable to real-time VMMR systems.Muhammad Haroon Yousaf received funding from the Higher Education Commission, Pakistan for Swarm Robotics Lab under the National Centre for Robotics and Automation (NCRA). The authors also acknowledge support from the Directorate of ASR& TD, University of Engineering and Technology Taxila, Pakistan

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