Efficient Human Detection Based on Parallel Implementation of Gradient and Texture Feature Extraction Methods (original) (raw)
Related papers
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2021
This paper proposes a scale-invariant histogram of oriented gradients (SI-HOG) for pedestrian detection. Most of the algorithms for pedestrian detection use the HOG as the basic feature and combine other features with the HOG to form the feature set, which is usually applied with a support vector machine (SVM). Hence, the HOG feature is the most efficient and fundamental feature for pedestrian detection. However, the HOG feature produces feature vectors of different lengths for different image resolutions; thus, the feature vectors are incomparable for the SVM. The proposed method forms a scale-space pyramid wherein the histogram bin is calculated. Thus, the gradient information from all the scales is encapsulated in a single fixed-length feature vector. The proposed method is also combined with color and texture features. The proposed approach is tested on three established benchmark pedestrian datasets: INRIA, NICTA, and Daimler. An improvement of ≥ 4.5% in the miss rate is achieved for all the three datasets considered. We also show that the SI-HOG can be applied to multiresolution datasets for which the HOG feature cannot be applied. Additionally, the MapReduce model is used to obtain the same outcome. The results indicate that the proposed approach outperforms the pedestrian-detection methods considered in this work.
Selection of Histograms of Oriented Gradients Features for Pedestrian Detection
Lecture Notes in Computer Science, 2008
Histograms of Oriented Gradients (HOG) is one of the wellknown features for object recognition. HOG features are calculated by taking orientation histograms of edge intensity in a local region. N.Dalal et al. proposed an object detection algorithm in which HOG features were extracted from all locations of a dense grid on a image region and the combined features are classified by using linear Support Vector Machine (SVM). In this paper, we employ HOG features extracted from all locations of a grid on the image as candidates of the feature vectors. Principal Component Analysis (PCA) is applied to these HOG feature vectors to obtain the score (PCA-HOG) vectors. Then a proper subset of PCA-HOG feature vectors is selected by using Stepwise Forward Selection (SFS) algorithm or Stepwise Backward Selection (SBS) algorithm to improve the generalization performance. The selected PCA-HOG feature vectors are used as an input of linear SVM to classify the given input into pedestrian/non-pedestrian. The improvement of the recognition rates are confirmed through experiments using MIT pedestrian dataset.
Enhancing Real-time Human Detection based on Histograms of Oriented Gradients
In this paper we propose a human detection framework based on an enhanced version of Histogram of Oriented Gradients (HOG) features. These feature descriptors are computed with the help of a precalculated histogram of square-blocks. This novel method outperforms the integral of oriented histograms allowing the calculation of a single feature four times faster. Using Adaboost for HOG feature selection and Support Vector Machine as weak classifier, we build up a real-time human classifier with an excellent detection rate.
Pedestrian Detection using Linear SVM Classifier with HOG Features
2018
Detecting pedestrians in an image is a useful technique in the development of the Intelligent Transportation System (ITS). Histogram of Oriented Gradient (HOG) is widely used as a feature algorithm and treated the entire body of the human as a single feature. In this paper, we compared the well-known feature based approaches: Haar features and HOG features in PSU pedestrian dataset under complex backgrounds, wide illuminations, large variation on pose and clothing. In practice, there are many complex movements and backgrounds in the real world environments. The experiment results show that a rich feature set supports the better detection performance. The performance of the detection results show that the accuracy of the HOG training model is 45.93% which is much better than Haar training model. Our proposed method can also apply to detect human in the real-world environment.
A pedestrian detector using histograms of oriented gradients and a Support Vector Machine classifier
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2007
This paper details filtering subsystem for a tetravision based pedestrian detection system. The complete system is based on the use of both visible and far infrared cameras; in an initial phase it produces a list of areas of attention in the images which can contain pedestrians. This list is furtherly refined using symmetry-based assumptions. Then, this results is fed to a number of independent validators that evaluate the presence of human shapes inside the areas of attention.
IFIP Advances in Information and Communication Technology, 2016
Pedestrian detection has lately attracted considerable interest from researchers due to many practical applications. However, the low accuracy and high complexity of pedestrian detection has still not enabled its use in successful commercial applications. In this chapter, we present insights into the complexity-accuracy relationship of pedestrian detection. We consider the Histogram of Oriented Gradients (HOG) scheme with linear Support Vector Machine (LinSVM) as a benchmark. We describe parallel implementations of various blocks of the pedestrian detection system which are designed for full-HD (1920x1080) resolution. Features are improved by optimal selection of cell size and histogram bins which have been shown to significantly affect the accuracy and complexity of pedestrian detection. It is seen that with a careful choice of these parameters a frame rate of 39.2 fps is achieved with a negligible loss in accuracy which is 16.3x and 3.8x higher than state of the art GPU and FPGA implementations respectively. Moreover 97.14% and 10.2% reduction in energy consumption is observed to process one frame. Finally, features are further enhanced by removing petty gradients in histograms which result in loss of accuracy. This increases the frame rate to 42.7 fps (18x and 4.1x higher) and lowers the energy consumption by 97.34% and 16.4% while improving the accuracy by 2% as compared to state of the art GPU and FPGA implementations respectively.
Fast pedestrian detection with multi-scale orientation features and two-stage classifiers
2010
In this paper, we propose an approach for fast pedestrian detection in images. Inspired by the histogram of oriented gradient (HOG) features, a set of multi-scale orientation (MSO) features are proposed as the feature representation. The features are extracted on square image blocks of various sizes (called units), containing coarse and fine features in which coarse ones are the unit orientations and fine ones are the pixel orientation histograms of the unit. A cascade of Adaboost is employed to train classifiers on the coarse features, aiming to high detection speed. A greedy searching algorithm is employed to select fine features, which are input into SVMs to train the fine classifiers, aiming to high detection accuracy. Experiments report that our approach obtains state-of-art results with 12.4 times faster than the SVM+HOG method.
Fast human detection with cascaded ensembles on the GPU
2010 IEEE Intelligent Vehicles Symposium, 2010
We investigate a fast pedestrian localization framework that integrates the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features on a data parallel architecture. The salient features of humans are captured by HoG blocks of variable sizes and locations which are chosen by the AdaBoost algorithm from a large set of possible blocks. We use the integral image representation for histogram computation and a rejection cascade in a slidingwindows manner, both of which can be implemented in a data parallel fashion. Utilizing the NVIDIA CUDA framework to realize this method on a Graphics Processing Unit (GPU), we report a speed up by a factor of 13 over our CPU implementation. For a 1280×960 image our parallel technique attains a processing speed of 2.5 to 8 frames per second depending on the image scanning density, which is similar to the recent GPU implementation of the original HoG algorithm in .
Cascade of Complementary Features for Fast and Accurate Pedestrian Detection
2010
We propose a cascade of two complementary features to detect pedestrians from static images quickly and accurately. Co-occurrence Histograms of Oriented Gradients (CoHOG) descriptors have a strong classification capability but are extremely high dimensional. On the other hand, Haar-like features are computationally efficient but not highly discriminative for extremely varying texture and shape information such as pedestrians with different clothing and stances. Therefore, the combination of both features enables fast and accurate pedestrian detection. Our framework comprises a cascade of Haar based weak classifiers followed by a CoHOG-SVM classifier. The experimental results on the DaimlerChrysler and INRIA benchmark datasets show that we can reach very close accuracy to the most accurate CoHOG-only classifier but in less than 1/200 of its computational cost. Additionally, we show that by integrating two of our proposed cascades: one full body with another upper body detectors, we can reach higher accuracy than the standalone full body CoHOG-only in about 1/100 of its computational cost.
Accelerated Histogram of Oriented Gradients for Human Detection
Dutse Journal of Pure and Applied Sciences
Histogram of Oriented Gradients (HOG) is an object detection algorithm used to detect people from an image. It involves features extraction called ‘HOG descriptor’ which are used to identify a person in the image. Several operations are involved in the feature extraction process. Hence performing numerous computations in order to obtain HOG descriptors takes some considerable amount of time. This slow computation speed limits HOG’s application in real-time systems. This paper investigates HOG with a view to improve its speed, modify the feature computation process to develop a faster version of HOG and finally evaluate against existing HOG. The technique of asymptotic notation in particular Big-O notation was applied to each stage of HOG and the complexity for the binning stage was modified. This results in a HOG version with a reduced complexity from n4 to n2 thereby having an improved speed as compared to the original HOG.