Histograms of oriented gradients for fast on-board vehicle verification

Histograms of Oriented Gradients (HoGs) provide excellent results in object detection and verification. However, their demanding processing requirements bound their applicability in some critical real-time scenarios, such as for video-based on-board vehicle detection systems. In this work, an efficient HOG configuration for pose-based on-board vehicle verification is proposed, which alleviates both the processing requirements and required feature vector length without reducing classification performance. The impact on classification of some critical configuration and processing parameters is in depth analyzed to propose a baseline efficient descriptor. Based on the analysis of its cells contribution to classification, new view-dependent cell-configuration patterns are proposed, resulting in reduced descriptors which provide an excellent balance between performance and computational requirements, rendering higher verification rates than other works in the literature.

Histogram of oriented gradients front end processing: An FPGA based processor approach

The Field Programmable Gate Array (FPGA) implementation of the commonly used Histogram of Oriented Gradients (HOG) algorithm is explored. The HOG algorithm is employed to extract features for object detection. A key focus has been to explore the use of a new FPGA-based processor which has been targeted at image processing. The paper gives details of the mapping and scheduling factors that influence the performance and the stages that were undertaken to allow the algorithm to be deployed on FPGA hardware, whilst taking into account the specific IPPro architecture features. We show that multi-core IPPro performance can exceed that of against state-of-the-art FPGA designs by up to 3.2 times with reduced design and implementation effort and increased flexibility all on a low cost, Zynq programmable system.

Human action recognition based on pyramid histogram of oriented gradients

Human action recognition has been attracted lots of interest from computer vision researchers due to its various promising applications. In this paper, we employ Pyramid Histogram of Orientation Gradient (PHOG) to characterize human figures for action recognition. Comparing to silhouette-based features, the PHOG descriptor does not require extraction of human silhouettes or contours. Two state-space models, i.e.; Hidden Markov Model (HMM) and Conditional Random Field (CRF), are adopted to model the dynamic human movement. The proposed PHOG descriptor and the state-space models with respect to different parameters are tested using a standard dataset. We also testify the robustness of the method with respect to various unconstrained conditions and viewpoints. Promising experimental result demonstrates the effectiveness and robustness of our proposed method.

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.

Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs

The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities.   HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.

A study of feature combination for vehicle detection based on image processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification.

V1-inspired features induce a weighted margin in SVMs

Image representations derived from simplified models of the primary visual cortex (V1), such as HOG and SIFT, elicit good performance in a myriad of visual classification tasks including object recognition/detection, pedestrian detection and facial expression classification. A central question in the vision, learning and neuroscience communities regards why these architectures perform so well. In this paper, we offer a unique perspective to this question by subsuming the role of V1-inspired features directly within a linear support vector machine (SVM). We demonstrate that a specific class of such features in conjunction with a linear SVM can be reinterpreted as inducing a weighted margin on the Kronecker basis expansion of an image. This new viewpoint on the role of V1-inspired features allows us to answer fundamental questions on the uniqueness and redundancies of these features, and offer substantial improvements in terms of computational and storage efficiency.

Efficient HOG human detection

While Histograms of Oriented Gradients (HOG) plus Support Vector Machine (SVM) (HOG+SVM) is the most successful human detection algorithm, it is time-consuming. This paper proposes two ways to deal with this problem. One way is to reuse the features in blocks to construct the HOG features for intersecting detection windows. Another way is to utilize sub-cell based interpolation to efficiently compute the HOG features for each block. The combination of the two ways results in significant increase in detecting humans-more than five times better. To evaluate the proposed method, we have established a top-view human database. Experimental results on the top-view database and the well-known INRIA data set have demonstrated the effectiveness and efficiency of the proposed method. (C) 2010 Elsevier B.V. All rights reserved.

In the pursuit of effective affective computing : the relationship between features and registration

For facial expression recognition systems to be applicable in the real world, they need to be able to detect and track a previously unseen person's face and its facial movements accurately in realistic environments. A highly plausible solution involves performing a "dense" form of alignment, where 60-70 fiducial facial points are tracked with high accuracy. The problem is that, in practice, this type of dense alignment had so far been impossible to achieve in a generic sense, mainly due to poor reliability and robustness. Instead, many expression detection methods have opted for a "coarse" form of face alignment, followed by an application of a biologically inspired appearance descriptor such as the histogram of oriented gradients or Gabor magnitudes. Encouragingly, recent advances to a number of dense alignment algorithms have demonstrated both high reliability and accuracy for unseen subjects [e.g., constrained local models (CLMs)]. This begs the question: Aside from countering against illumination variation, what do these appearance descriptors do that standard pixel representations do not? In this paper, we show that, when close to perfect alignment is obtained, there is no real benefit in employing these different appearance-based representations (under consistent illumination conditions). In fact, when misalignment does occur, we show that these appearance descriptors do work well by encoding robustness to alignment error. For this work, we compared two popular methods for dense alignment-subject-dependent active appearance models versus subject-independent CLMs-on the task of action-unit detection. These comparisons were conducted through a battery of experiments across various publicly available data sets (i.e., CK+, Pain, M3, and GEMEP-FERA). We also report our performance in the recent 2011 Facial Expression Recognition and Analysis Challenge for the subject-independent task.

Ion beam processing of oriented CuO films deposited on (100) YSZ by laser ablation

A systematic study of Ar ion implantation in cupric oxide films has been reported. Oriented CuO films were deposited by pulsed excimer laser ablation technique on (1 0 0) YSZ substrates. X-ray diffraction (XRD) spectra showed the highly oriented nature of the deposited CuO films. The films were subjected to ion bombardment for studies of damage formation, Implantations were carried out using 100 keV Arf over a dose range between 5 x 10(12) and 5 x 10(15) ions/cm(2). The as-deposited and ion beam processed samples were characterized by XRD technique and resistance versus temperature (R-T) measurements. The activation energies for electrical conduction were found from In [R] versus 1/T curves. Defects play an important role in the conduction mechanism in the implanted samples. The conductivity of the film increases, and the corresponding activation energy decreases with respect to the dose value.

On Visual Detection of Highly-occluded Objects for Harvesting Automation in Horticulture

Developing accurate and reliable crop detection algorithms is an important step for harvesting automation in horticulture. This paper presents a novel approach to visual detection of highly-occluded fruits. We use a conditional random field (CRF) on multi-spectral image data (colour and Near-Infrared Reflectance, NIR) to model two classes: crop and background. To describe these two classes, we explore a range of visual-texture features including local binary pattern, histogram of oriented gradients, and learn auto-encoder features. The pro-posed methods are evaluated using hand-labelled images from a dataset captured on a commercial capsicum farm. Experimental results are presented, and performance is evaluated in terms of the Area Under the Curve (AUC) of the precision-recall curves.Our current results achieve a maximum performance of 0.81AUC when combining all of the texture features in conjunction with colour information.

NMR spectra of oriented biologically important molecules - The structure of and the internal rotation in N,N'-dimethyluracil

From the proton NMR spectra of Nfl-dimethyluracil oriented in two different nematic solvents, the internal rotation of the methyl groups about the N-C bonds is studied. It has been observed that the preferred conformation of the methyl group having one carbonyl in the vicinity is the one where a C-H bond is in the ring plane pointing toward the carbonyl group. The results are not sensitive to the mode of rotation of the other methyl group. These data are interpreted in terms of the bond polarizations.

Electrochemical Reduction of Oriented Graphene Oxide Films: An in Situ Raman Spectroelectrochemical Study

Graphene oxide (GO) is assembled on a gold substrate by a layer-by-layer technique using a self-assembled cystamine monolayer. The negatively charged GO platelets are attached to the positively charged cystamine monolayer through electrostatic interactions. Subsequently, it is shown that the GO can be reduced electrochemically using applied DC bias by scanning the potential from 0 to -1 V vs a saturated calomel electrode in an aqueous electrolyte. The GO and reduced graphene oxide (RGO) are characterized by Raman spectroscopy and atomic force microscopy (AFM). A clear shift of the G band from 1610 cm-1 of GO to 1585 cm-1 of RGO is observed. The electrochemical reduction is followed in situ by micro Raman spectroscopy by carrying out Raman spectroscopic studies during the application of DC bias. The GO and RGO films have been characterized by conductive AFM that shows an increase in the current flow by at least 3 orders of magnitude after reduction. The electrochemical method of reducing GO may open up another way of controlling the reduction of GO and the extent of reduction to obtain highly conducting graphene on electrode materials.

Nuclear magnetic resonance spectra of oriented bicyclic systems containing heteroatom(s):the spectrum of 2-thiocoumarin

From the proton nmr studies of 2-thiocoumarin and coumarin, it is concluded that the relative interproton distances in the two oxygen heteroatom bicyclic systems are similar. The values for the phenyl ring protons do not deviate significantly from the regular hexagonal geometry, unlike bicyclic systems with nitrogens as the heteroatoms, such as diazanaphthalenes. Larger values of the indirect spin-spin couplings within the protons of the ring containing the oxygen heteroatom, compared to the values between the ortho protons in the phenyl rings in coumarin and 2-thiocoumarin, correspond to the olefinic nature of these protons. This is in contrast to results for the nitrogen heterocycles where both the rings are aromatic.