Histogram of oriented gradients

About: Histogram of oriented gradients is a research topic. Over the lifetime, 2037 publications have been published within this topic receiving 55881 citations. The topic is also known as: HOG.

Robust visual tracking based on generative and discriminative model collaboration

Abstract: Effective object appearance model is one of the key issues for the success of visual tracking. Since the appearance of a target and the environment changes dynamically, the majority of existed visual tracking algorithms tend to drift away from targets. To address this issue, we propose a robust tracking algorithm by integrating the generative and discriminative model. The object appearance model is made up of generative target model and a discriminative classifier. For the generative target model, we adopt the weighted structural local sparse appearance model combining patch based gray value and Histogram of Oriented Gradients feature as the patch dictionary. By sampling positives and negatives, alignment-pooling features are obtained based on the patch dictionary through local sparse coding, then we use support vector machine to train the discriminative classifier. The proposed method is embedded into a Bayesian inference framework for visual tracking. A combined matching method is adopted to improve the proposal distribution of the particle filter. Moreover, in order to adapt the situation change, the patch dictionary and discriminative classifier are updated by incremental learning every five frames. Experimental results on some publicly available benchmarks of video sequences demonstrate the accuracy and effectiveness of our tracker.

Bangla Compound Character Recognition using Support Vector Machine (SVM) on Advanced Feature Sets

Abstract: A systematic approach for handwritten character detection and recognition using SVM has many significant applications. The proposed technique extracts features to classify the complex structural variations of Bangla compound characters. The three features used for classification are - Longest Run Feature (LRF) based on CG based Partitioning, Histogram of Oriented Gradients (HOG) Feature and Diagonal Feature. This enhanced, powerful combination of features result in a 180-length variable feature vector for each character, which is found to be adequate enough to uniquely represent and identify each character. Prior, Bangla handwritten character recognition problem has not been addressed with the proposed feature extraction techniques. The extracted feature vectors are used during the training phase for building a SVM classifier, testing phase for classifying characters which are then compared with their respective labels during evaluation. The results obtained show higher efficiency regarding classifier accuracy as compared to other existing approaches.

Detecting People Using Histogram of Oriented Gradients: A Step towards Abnormal Human Activity Detection

Abstract: Human activity understanding is a branch of research in computer vision that has attracted a lot of attention for decades. Accurate identification of humans in video surveillance is fundamental prerequisite towards activities’ understanding. Little or no research has been conducted for human detection in financial endpoint premises specifically Automatic Teller Machine (ATM) sceneries. The video surveillance settings have some unique features compared to others applications: static and non-uniform background, low resolution images, and lack of initial background model. The Histogram of oriented gradient technique was used to locate people in each frame of the surveillance video. Our framework achieved a precision of 88.71 and F-score of 56.41.

Real-Time Image Resizing Hardware Accelerator for Object Detection Algorithms

Abstract: This paper describes motivation and hardware architecture for resizing input image frames from the camera in order to support real-time scale-invariant object recognition. Conventional implementation of object detection algorithms such as histogram of oriented gradients (HOG) based feature extraction, face detection using Haar classifiers often perform image resizing during the object recognition process. Our investigation reveals that this incurs significant performance overhead due to frequent memory accesses that are required for image resizing. This has motivated our approach to perform online resizing - simultaneously resizing of the input image when it is loaded into frame buffer memory - prior to the object recognition process. We propose a hardware architecture to accelerate image resizing and describe a hybrid processor-accelerator platform to generate different sizes of an image in real-time for object recognition.

Classification of vehicles for urban traffic scenes

Abstract: An investigation into detection and classification of vehicles and pedestrians from video in urban traffic scenes is presented. The final aim is to produce systems to guide surveillance operators and reduce human resources for observing hundreds of cameras in urban traffic surveillance. Cameras are a well established means for traffic managers to observe traffic states and improve journey experiences. Firstly, per frame vehicle detection and classification is performed using 3D models on calibrated cameras. Motion silhouettes (from background estimation) are extracted and compared to a projected model silhouette to identify the ground plane position and class of vehicles and pedestrians. The system has been evaluated with the reference i-LIDS data sets from the UK Home Office. Performance has been compared for varying numbers of classes, for three different weather conditions and for different video input filters. The full system including detection and classification achieves a recall of 87% at a precision of 85.5% outperforming similar systems in the literature. To improve robustness, the use of local image patches to incorporate object appearance is investigated for surveillance applications. As an example, a novel texture saliency classifier has been proposed to detect people in a video frame by identifying salient texture regions. The image is classified into foreground and background in real- time.No temporal image information is used during the classification. The system, used for the task of detecting people entering a sterile zone, a common scenario for visual surveillance. Testing has been performed on the i-LIDS sterile zone benchmark data set of the UK Home Qffice. The basic detector is extended by fusing its output with simple motion infonriation, which significantly outperforms standard motion tracking. Lower detection time can be achieved by combining texture classification with Kalman filtering. The fusion approach running on 10 frames per second gives the highest result of Fl=O.92 for the 24 hour test data set. Based on the good results for local features, a novel classifier has been introduced by combining the concept of 3D models with local features to overcome limitations of conventional silhouette-based methods and local features in 2D. The appearance of vehicles varies substantially with the viewing angle and local features may often be occluded. In this thesis, full 3D models are used for the object categories to be detected and the feature patches are defined over these models. A calibrated camera allows an affine transformation of the observation into a normalised representation from which '3DHOG' features (3D extended histogram of oriented gradients) are defined. A variable set of interest points is used in the detection and classification processes, depending on which points in the 3D model are visible. The 3DHOG feature is compared with features based on FFf and simple histograms and also to the motion silhouette baseline on the same data. The results demonstrate that the proposed method achieves comparable performance. In particular, an advantage of .the proposed, method is that it is robust against miss-Shaped motion silhouettes which can be caused by variable lighting, camera quality and occlusions from other objects. The proposed algorithms are evaluated further on a new data set from a different camera with higher resolution, which demonstrates the portability of the training data to novel camera views. Kalman filter tracking is introduced to gain trajectory information, is used for behaviour analysis. Correctly detected tracks of 94% outperforming a baseline motion tracker (OpenCV) tested under the same conditions. A demonstrator for bus lane monitoring is introduced using the output of the detection and classification system. The thesis concludes with a critical analysis of the work and the outlook for future research opportunities.