Silhouette-based human action recognition with a multi-class support vector machine

This paper has been presented at : 9th International Conference on Pattern Recognition Systems (ICPRS 2018)Computer vision systems have become increasingly popular, being used to solve a wide range of problems. In this paper, a computer vision algorithm with a support vector machine (SVM) classifier is presented. The work focuses on the recognition of human actions through computer vision, using a multi-camera dataset of human actions called MuHAVi. The algorithm uses a method to extract features, based on silhouettes. The challenge is that in MuHAVi these silhouettes are noisy and in many cases include shadows. As there are many actions that need to be recognised, we take a multiclass classification ap-proach that combines binary SVM classifiers. The results are compared with previous results on the same dataset and show a significant improvement, especially for recognising actions on a different view, obtaining overall accuracy of 85.5% and of 93.5% for leave-one-camera-out and leave-one-actor-out tests respectively.Sergio A Velastin has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander

Feature selection using correlation analysis and principal component analysis for accurate breast cancer diagnosis

Breast cancer is one of the leading causes of death among women, more so than all other cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the disease, changing treatment procedures and different patient population samples. Diagnostic techniques with better performance are very important for personalized care and treatment and to reduce and control the recurrence of cancer. The main objective of this research was to select feature selection techniques using correlation analysis and variance of input features before passing these significant features to a classification method. We used an ensemble method to improve the classification of breast cancer. The proposed approach was evaluated using the public WBCD dataset (Wisconsin Breast Cancer Dataset). Correlation analysis and principal component analysis were used for dimensionality reduction. Performance was evaluated for well-known machine learning classifiers, and the best seven classifiers were chosen for the next step. Hyper-parameter tuning was performed to improve the performances of the classifiers. The best performing classification algorithms were combined with two different voting techniques. Hard voting predicts the class that gets the majority vote, whereas soft voting predicts the class based on highest probability. The proposed approach performed better than state-of-the-art work, achieving an accuracy of 98.24%, high precision (99.29%) and a recall value of 95.89%

PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos

This letter proposes a method for the generation of temporal action proposals for the segmentation of long uncut video sequences. The presence of consecutive multiple actions in video sequences makes the temporal segmentation a challenging problem due to the unconstrained nature of actions in space and time. To address this issue, we exploit the nonaction segments present between the actual human actions in uncut videos. From the long uncut video, we compute the energy of consecutive nonoverlapping motion history images (MHIs), which provides spatiotemporal information of motion. Our proposals from MHIs (PMHI) are based on clustering the MHIs into actions and nonaction segments by detecting minima from the energy of MHIs. PMHI efficiently segments the long uncut videos into a small number of nonoverlapping temporal action proposals. The strength of PMHI is that it is unsupervised, which alleviates the requirement for any training data. Our temporal action proposal method outperforms the existing proposal methods on the Multi-view Human Action video (MuHAVi)-uncut and Computer Vision and Pattern recognition (CVPR) 2012 Change Detection datasets with an average recall rate of 86.1% and 86.0%, respectively.Sergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement nº 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santande

DA-VLAD: Discriminative action vector of locally aggregated descriptors for action recognition

This paper has been presented at : 25th IEEE International Conference on Image Processing (ICIP 2018)In this paper, we propose a novel encoding method for the representation of human action videos, that we call Discriminative Action Vector of Locally Aggregated Descriptors (DA-VLAD). DA-VLAD is motivated by the fact that there are many unnecessary and overlapping frames that cause non-discriminative codewords during the training process. DA-VLAD deals with this issue by extracting class-specific clusters and learning the discriminative power of these codewords in the form of informative weights. We use these discriminative action weights with standard VLAD encoding as a contribution of each codeword. DA-VLAD reduces the inter-class similarity efficiently by diminishing the effect of common codewords among multiple action classes during the encoding process. We present the effectiveness of DA-VLAD on two challenging action recognition datasets: UCF101 and HMDB51, improving the state-of-the-art with accuracies of 95.1% and 80.1% respectively.We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research. We also acknowledge the support from the Directorate of Advance Studies, Research and Technological development (ASR) & TD, University of Engineering and Technology Taxila, Pakistan. Sergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement n 600371, el Ministerio de Economia y Competitividad (COFUND2013-51509) and Banco Santander

Characterisation of the spatial sensitivity of classifiers in pedestrian detection

This paper has been presented at : 6th LatinAmerican Conference on Networked Electronic Media (LACNEM-2015)In this paper, a study of the spatial sensitivity in the pedestrian detection context is carried out by a comparison of two descriptor-classifier combinations, using the well-known sliding window approach and looking for a well-tuned response of the detector. By well-tuned, we mean that multiple detections are minimised so as to facilitate the usual non-maximal suppression stage. So, to guide the evaluation we introduce the concept of spatial sensitivity so that a pedestrian detection algorithm with good spatial sensitivity can reduce the number of classifications in the pedestrian neighbourhood, ideally to one. To characterise spacial sensitivity we propose and use a new metric to measure it. Finally we carry out a statistical analysis (ANOVA) to validate the results obtained from the metric usage.The work described here was carried out as part of the OB-SERVE project funded by the Fondecyt Regular programme of Conicyt (Chilean Research Council for Science and Technology) under grant no. 1140209. Sergio A Velastin has re-ceived funding from the Universidad Carlos III de Madrid, the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement no 600371, el Ministerio de Economa y Competitividad (COFUND2013-51509) el Ministerio de Educacin, cultura y Deporte (CEI-15-17) and Banco Santander

An Optimized and Fast Scheme for Real-time Human Detection using Raspberry Pi

This paper has been presented at : The International Conference on Digital Image Computing: Techniques and Applications (DICTA 2016)Real-time human detection is a challenging task due to appearance variance, occlusion and rapidly changing content; therefore it requires efficient hardware and optimized software. This paper presents a real-time human detection scheme on a Raspberry Pi. An efficient algorithm for human detection is proposed by processing regions of interest (ROI) based upon foreground estimation. Different number of scales have been considered for computing Histogram of Oriented Gradients (HOG) features for the selected ROI. Support vector machine (SVM) is employed for classification of HOG feature vectors into detected and non-detected human regions. Detected human regions are further filtered by analyzing the area of overlapping regions. Considering the limited capabilities of Raspberry Pi, the proposed scheme is evaluated using six different testing schemes on Town Centre and CAVIAR datasets. Out of these six testing schemes, Single Window with two Scales (SW2S) processes 3 frames per second with acceptable less accuracy than the original HOG. The proposed algorithm is about 8 times faster than the original multi-scale HOG and recommended to be used for real-time human detection on a Raspberry Pi

Feature Similarity and Frequency-Based Weighted Visual Words Codebook Learning Scheme for Human Action Recognition

This paper has been presented at : 8th Pacific-Rim Symposium, PSIVT 2017.Human action recognition has become a popular field for computer vision researchers in the recent decade. This paper presents a human action recognition scheme based on a textual information concept inspired by document retrieval systems. Videos are represented using a commonly used local feature representation. In addition, we formulate a new weighted class specific dictionary learning scheme to reflect the importance of visual words for a particular action class. Weighted class specific dictionary learning enriches the scheme to learn a sparse representation for a particular action class. To evaluate our scheme on realistic and complex scenarios, we have tested it on UCF Sports and UCF11 benchmark datasets. This paper reports experimental results that outperform recent state-of-the-art methods for the UCF Sports and the UCF11 dataset i.e. 98.93% and 93.88% in terms of average accuracy respectively. To the best of our knowledge, this contribution is first to apply a weighted class specific dictionary learning method on realistic human action recognition datasets.Sergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement n 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. Authors also acknowledges support from the Directorate of ASR and TD, University of Engineering and Technology Taxila, Pakistan

Multi-view Human Action Recognition using Histograms of Oriented Gradients (HOG) Description of Motion History Images (MHIs)

This paper has been presented at : 13th International Conference on Frontiers of Information Technology (FIT)In this paper, a silhouette-based view-independent human action recognition scheme is proposed for multi-camera dataset. To overcome the high-dimensionality issue, incurred due to multi-camera data, the low-dimensional representation based on Motion History Image (MHI) was extracted. A single MHI is computed for each view/action video. For efficient description of MHIs Histograms of Oriented Gradients (HOG) are employed. Finally the classification of HOG based description of MHIs is based on Nearest Neighbor (NN) classifier. The proposed method does not employ feature fusion for multi-view data and therefore this method does not require a fixed number of cameras setup during training and testing stages. The proposed method is suitable for multi-view as well as single view dataset as no feature fusion is used. Experimentation results on multi-view MuHAVi-14 and MuHAVi-8 datasets give high accuracy rates of 92.65% and 99.26% respectively using Leave-One-Sequence-Out (LOSO) cross validation technique as compared to similar state-of-the-art approaches. The proposed method is computationally efficient and hence suitable for real-time action recognition systems.S.A. Velastin acknowledges funding from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement n° 600371, el Ministerio de Economia y Competitividad (COFUND2013-51509) and Banco Santander

Inter and Intra Class Correlation Analysis (IIcCA) for Human Action Recognition in Realistic Scenarios

This paper has been presented at : 8th International Conference of Pattern Recognition Systems (ICPRS 2017)Human action recognition in realistic scenarios is an important yet challenging task. In this paper we propose a new method, Inter and Intra class correlation analysis (IICCA), to handle inter and intra class variations observed in realistic scenarios. Our contribution includes learning a class specific visual representation that efficiently represents a particular action class and has a high discriminative power with respect to other action classes. We use statistical measures to extract visual words that are highly intra correlated and less inter correlated. We evaluated and compared our approach with state-of-the-art work using a realistic benchmark human action recognition dataset.S.A. Velastin has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 600371, the Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) the Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area