Real-time human action recognition on an embedded, reconfigurable video processing architecture

Copyright @ 2008 Springer-Verlag.In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine (SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. “motion history image”) class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfiured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments.DTI and Broadcom Ltd

Beam-Energy-Dependent Two-Pion Interferometry and the Freeze-Out Eccentricity of Pions Measured in Heavy Ion Collisions at the Star Detector

We present results of analyses of two-pion interferometry in Au + Au collisions at root s(NN) = 7.7, 11.5, 19.6, 27, 39, 62.4, and 200 GeV measured in the STAR detector as part of the BNL Relativistic Heavy Ion Collider Beam Energy Scan program. The extracted correlation lengths (Hanbury-Brown-Twiss radii) are studied as a function of beam energy, azimuthal angle relative to the reaction plane, centrality, and transverse mass (m(T)) of the particles. The azimuthal analysis allows extraction of the eccentricity of the entire fireball at kinetic freeze-out. The energy dependence of this observable is expected to be sensitive to changes in the equation of state. A new global fit method is studied as an alternate method to directly measure the parameters in the azimuthal analysis. The eccentricity shows a monotonic decrease with beam energy that is qualitatively consistent with the trend from all model predictions and quantitatively consistent with a hadronic transport model

A Data Fusion Perspective on Human Motion Analysis Including Multiple Camera Applications

Proceedings of: 5th International Work-Conference on the Interplay Between Natural and Artificial Computation, (IWINAC 2013). Mallorca, Spain, June 10-14.Human motion analysis methods have received increasing attention during the last two decades. In parallel, data fusion technologies have emerged as a powerful tool for the estimation of properties of objects in the real world. This papers presents a view of human motion analysis from the viewpoint of data fusion. JDL process model and Dasarathy's input-output hierarchy are employed to categorize the works in the area. A survey of the literature in human motion analysis from multiple cameras is included. Future research directions in the area are identified after this review.Publicad

Gait-based gender classification using persistent homology

In this paper, a topological approach for gait-based gender recognition is presented. First, a stack of human silhouettes, extracted by background subtraction and thresholding, were glued through their gravity centers, forming a 3D digital image I. Second, different filters (i.e. particular orders of the simplices) are applied on ∂ K(I) (a simplicial complex obtained from I) which capture relations among the parts of the human body when walking. Finally, a topological signature is extracted from the persistence diagram according to each filter. The measure cosine is used to give a similarity value between topological signatures. The novelty of the paper is a notion of robustness of the provided method (which is also valid for gait recognition). Three experiments are performed using all human-camera view angles provided in CASIA-B database. The first one evaluates the named topological signature obtaining 98.3% (lateral view) of correct classification rates, for gender identification. The second one shows results for different human-camera distances according to training and test (i.e. training with a human-camera distance and test with a different one). The third one shows that upper body is more discriminative than lower body

Invariance in linear systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32794/1/0000167.pd

Improving the Segmentation Stage of a Pedestrian Tracking Video-based System by means of Evolution Strategies

12 pages, 7 figures.-- Contributed to: Eighth European Workshop on Evolutionary Computation in Image Analysis and Signal Processing (EvoIASP 2006, Budapest, Hungary, Apr 10-12, 2006).Pedestrian tracking video-based systems present particular problems such as the multi fragmentation or low level of compactness of the resultant blobs due to the human shape or movements. This paper shows how to improve the segmentation stage of a video surveillance system by adding morphological post-processing operations so that the subsequent blocks increase their performance. The adjustment of the parameters that regulate the new morphological processes is tuned by means of Evolution Strategies. Finally, the paper proposes a group of metrics to assess the global performance of the surveillance system. After the evaluation over a high number of video sequences, the results show that the shape of the tracks match up more accurately with the parts of interests. Thus, the improvement of segmentation stage facilitates the subsequent stages so that global performance of the surveillance system increases.Funded by CICYT (TIC2002-04491-C02-02)Publicad

Photon interferometry and size of the hot zone in relativistic heavy ion collisions

The parameters obtained from the theoretical analysis of the single photon spectra observed by the WA98 collaboration at SPS energies have been used to evaluate the two photon correlation functions. The single photon spectra and the two photon correlations at RHIC energies have also been evaluated, taking into account the effects of the possible spectral change of hadrons in a thermal bath. We find that the ratio $R_{side}/R_{out} \sim 1$ for SPS and $R_{side}/R_{out} <1$ for RHIC energy.Comment: text changed, figures adde

Egocentric activity monitoring and recovery

This paper presents a novel approach for real-time egocentric activity recognition in which component atomic events are characterised in terms of binary relationships between parts of the body and manipulated objects. The key contribution is to summarise, within a histogram, the relationships that hold over a fixed time interval. This histogram is then classified into one of a number of atomic events. The relationships encode both the types of body parts and objects involved (e.g. wrist, hammer) together with a quantised representation of their distance apart and the normalised rate of change in this distance. The quantisation and classifier are both configured in a prior learning phase from training data. An activity is represented by a Markov model over atomic events. We show the application of the method in the prediction of the next atomic event within a manual procedure (e.g. assembling a simple device) and the detection of deviations from an expected procedure. This could be used for example in training operators in the use or servicing of a piece of equipment, or the assembly of a device from components. We evaluate our approach (’Bag-of-Relations’) on two datasets: ‘labelling and packaging bottles’ and ‘hammering nails and driving screws’, and show superior performance to existing Bag-of-Features methods that work with histograms derived from image features [1]. Finally, we show that the combination of data from vision and inertial (IMU) sensors outperforms either modality alone

A 3D Human Posture Approach for Activity Recognition Based on Depth Camera

Human activity recognition plays an important role in the context of Ambient Assisted Living (AAL), providing useful tools to improve people quality of life. This work presents an activity recognition algorithm based on the extraction of skeleton joints from a depth camera. The system describes an activity using a set of few and basic postures extracted by means of the X-means clustering algorithm. A multi-class Support Vector Machine, trained with the Sequential Minimal Optimization is employed to perform the classification. The system is evaluated on two public datasets for activity recognition which have different skeleton models, the CAD-60 with 15 joints and the TST with 25 joints. The proposed approach achieves precision/recall performances of 99.8 % on CAD-60 and 97.2 %/91.7 % on TST. The results are promising for an applied use in the context of AAL