Action Recognition Using Particle Flow Fields

In recent years, research in human action recognition has advanced on multiple fronts to address various types of actions including simple, isolated actions in staged data (e.g., KTH dataset), complex actions (e.g., Hollywood dataset), and naturally occurring actions in surveillance videos (e.g, VIRAT dataset). Several techniques including those based on gradient, flow, and interest-points, have been developed for their recognition. Most perform very well in standard action recognition datasets, but fail to produce similar results in more complex, large-scale datasets. Action recognition on large categories of unconstrained videos taken from the web is a very challenging problem compared to datasets like KTH (six actions), IXMAS (thirteen actions), and Weizmann (ten actions). Challenges such as camera motion, different viewpoints, huge interclass variations, cluttered background, occlusions, bad illumination conditions, and poor quality of web videos cause the majority of the state-of-the-art action recognition approaches to fail. An increasing number of categories and the inclusion of actions with high confusion also increase the difficulty of the problem. The approach taken to solve this action recognition problem depends primarily on the dataset and the possibility of detecting and tracking the object of interest. In this dissertation, a new method for video representation is proposed and three new approaches to perform action recognition in different scenarios using varying prerequisites are presented. The prerequisites have decreasing levels of difficulty to obtain: 1) Scenario requires human detection and trackiii ing to perform action recognition; 2) Scenario requires background and foreground separation to perform action recognition; and 3) No pre-processing is required for action recognition. First, we propose a new video representation using optical flow and particle advection. The proposed “Particle Flow Field” (PFF) representation has been used to generate motion descriptors and tested in a Bag of Video Words (BoVW) framework on the KTH dataset. We show that particle flow fields has better performance than other low-level video representations, such as 2D-Gradients, 3D-Gradients and optical flow. Second, we analyze the performance of the state-of-the-art technique based on the histogram of oriented 3D-Gradients in spatio temporal volumes, where human detection and tracking are required. We use the proposed particle flow field and show superior results compared to the histogram of oriented 3D-Gradients in spatio temporal volumes. The proposed method, when used for human action recognition, just needs human detection and does not necessarily require human tracking and figure centric bounding boxes. It has been tested on KTH (six actions), Weizmann (ten actions), and IXMAS (thirteen actions, 4 different views) action recognition datasets. Third, we propose using the scene context information obtained from moving and stationary pixels in the key frames, in conjunction with motion descriptors obtained using Bag of Words framework, to solve the action recognition problem on a large (50 actions) dataset with videos from the web. We perform a combination of early and late fusion on multiple features to handle the huge number of categories. We demonstrate that scene context is a very important feature for performing action recognition on huge datasets. iv The proposed method needs separation of moving and stationary pixels, and does not require any kind of video stabilization, person detection, or tracking and pruning of features. Our approach obtains good performance on a huge number of action categories. It has been tested on the UCF50 dataset with 50 action categories, which is an extension of the UCF YouTube Action (UCF11) Dataset containing 11 action categories. We also tested our approach on the KTH and HMDB51 datasets for comparison. Finally, we focus on solving practice problems in representing actions by bag of spatio temporal features (i.e. cuboids), which has proven valuable for action recognition in recent literature. We observed that the visual vocabulary based (bag of video words) method suffers from many drawbacks in practice, such as: (i) It requires an intensive training stage to obtain good performance; (ii) it is sensitive to the vocabulary size; (iii) it is unable to cope with incremental recognition problems; (iv) it is unable to recognize simultaneous multiple actions; (v) it is unable to perform recognition frame by frame. In order to overcome these drawbacks, we propose a framework to index large scale motion features using Sphere/Rectangle-tree (SR-tree) for incremental action detection and recognition. The recognition comprises of the following two steps: 1) recognizing the local features by non-parametric nearest neighbor (NN), and 2) using a simple voting strategy to label the action. It can also provide localization of the action. Since it does not require feature quantization it can efficiently grow the feature-tree by adding features from new training actions or categories. Our method provides an effective way for practical incremental action recognition. Furthermore, it can handle large scale datasets because the SR-tree is a disk-based v data structure. We tested our approach on two publicly available datasets, the KTH dataset and the IXMAS multi-view dataset, and achieved promising results

New contention resolution techniques for optical burst switching

Optical burst switching (OBS) is a technology positioned between wavelength routing and optical packet switching that does not require optical buffering or packet-level parsing, and it is more efficient than circuit switching when the sustained traffic volume does not consume a full wavelength. However, several critical issues still need to be solved such as contention resolution without optical buffering which is a key determinant of packet-loss with a significant impact on network performance. Deflection routing is an approach for resolving contention by routing a contending packet to an output port other than the intended output port. In OBS networks, when contention between two bursts cannot be resolved through deflection routing, one of the bursts will be dropped. However, this scheme doesn’t take advantage of all the available resources in resolving contentions. Due to this, the performance of existing deflection routing scheme is not satisfactory. In this thesis, we propose and evaluate three new strategies which aim at resolving contention. We propose a new approach called Backtrack on Deflection Failure, which provides a second chance to blocked bursts when deflection failure occurs. The bursts in this scheme, when blocked, will get an opportunity to backtrack to the previous node and may get routed through any deflection route available at the previous node. Two variants are proposed for handling the backtracking delay involved in this scheme namely: (a) Increase in Initial Offset and (b) Open-Loop Reservation. Furthermore, we propose a third scheme called Bidirectional Reservation on Burst Drop in which bandwidth reservation is made in both the forward and the backward directions simultaneously. This scheme comes into effect only when control bursts get dropped due to bandwidth unavailability. The retransmitted control bursts will have larger offset value and because of this, they will have lower blocking probability than the original bursts. The performance of our schemes and of those proposed in the literature is studied through simulation. The parameters considered in evaluating these schemes are blocking probability, average throughput, and overall link utilization. The results obtained show that our schemes perform significantly better than their standard counterparts

A petri net toolkit for parallel program debugging

An effective debugger must support the language and operating system resource abstractions that are available to the programmer. Earlier debuggers worked at the machine architecture level: they dealt with machine instructions and registers. Current debuggers, designed for single process debugging, permit access to program variables and breakpoints and single-stepping at the level of high-level language statements. Eventhough the current debuggers, are already implemented to be a powerful tool, they still cannot do a job of parallel debugger. In this thesis, a computer simulation system has been established by Petri Nets execution providing a convenient and friendly interface as it allows the user to do parallel program debugging. The Parallel Debugger is simulated by providing a time parameter for each transition and thus simulating the net performance. Hitherto, this time parameter can either be constant or exponentially distributed

COMPUTATIONAL STUDIES ON DROPLET DYNAMICS AT INTERSECTING FLOWS IN MICROFLUIDIC JUNCTIONS

The current thesis involves a computational study of drop dynamics in microfluidic junctions, at the moderate capillary number of Ca = 0.1. We utilize a three-dimensional Spectral Boundary Element algorithm to determine the drop motion in the presence of intersecting lateral flows in microfluidic, T-junctions and cross-junctions, and analyze the effect on drop deformation and motion with varying shear rates in the channels leading to the junctions, and for viscosity ratios of 0.2 and 20.0 between the drop and the surrounding fluid. We find that the presence of intersecting flows, drastically affects the transient behavior at the junctions, and the drop reaches steady state further away, both up- stream and downstream of these junctions. The time taken to reach steady state in the T-junctions was found to be significantly greater than that in the cross-junction, under identical conditions. Drop velocities were found to be a linear function of the effective shear rate in the channel, and length scale fluctuations as high as 30 percent were observed in the junction region for the cases studied in the thesis. We observed that the excess presure drop with respect to the flow of a single phase fluid was strongly related to the length of the droplet at a given spatial coordinate. The peak surface area of the drop in the junction was found to be a slighly non-linear function of the flow rates in the lateral channels, and almost all the surface area increase was occurring at the head of the drop, in the direction of the flow. Velocity was found to be a weak, inverse function of the viscosity ratio, the increase in drop surface area was found to be greater in drops with lower viscosity. It was found that the junction bend radius/smoothness had a more significant effect on the dynamics of the drop in a T-junction, compared to that in a cross-junction

Significance of Mesoscale Warm Core Eddy on Marine and Coastal Environment of the Bay of Bengal

Bay of Bengal (BoB) is an affluent region for the mesoscale (eddies) and synoptic scale (cyclones) systems. It occurs primarily through the seasonal variations, dynamical instabilities and equatorial wind forcing mechanisms. The individual or cumulative effect of these changes is vulnerable to the coastal and marine ecosystems. For example, tropical cyclone (TC) AILA experienced a warm core eddy (WCE) before the landfall, and consequently it intensified into a severe cyclonic storm (CS) and remained as a CS up to 15 h after the landfall. Its severity produces a heavy rainfall of >18 cm day−1, thus leads to the coastal flooding. The eddy contribution to the TC is witnessed during and after the landfall. Inappropriately, high resolution in-situ observations are not available to identify such important processes on different time and spatial scales. Therefore, the present chapter analyses the northern BoB eddy induced signals using both in-situ and satellite (advanced microwave scanning radiometer—AMSR-2) derived products. Two in-situ locations (BD08 and BD09) are employed for this study purpose. The eddy responses at no-eddy, during and after eddy, have been analyzed. Besides, WCE imprints on the overlying atmosphere are also observed. The relationship between sea surface temperature and wind speed over the BoB region is assessed

Impact of obesity with impaired glucose tolerance on retinal degeneration in a rat model of metabolic syndrome.

PurposeMetabolic syndrome (MetS) is associated with several degenerative diseases, including retinal degeneration. Previously, we reported on progressive retinal degeneration in a spontaneous obese rat (WNIN/Ob) model. In this study, we investigated the additional effect of impaired glucose tolerance (IGT), an essential component of MetS, on retinal degeneration using the WNIN/GR-Ob rat model.MethodsThe retinal morphology and ultrastructure of WNIN/GR-Ob and age-matched littermate lean rats were studied by microscopy and immunohistochemistry. The retinal transcriptome of WNIN/GR-Ob was compared with the respective lean controls and with the WNIN/Ob model using microarray analysis. Expression of selected retinal marker genes was studied via real-time PCR.ResultsProgressive loss of photoreceptor cells was observed in WNIN/GR-Ob rats with an onset as early as 3 months. Similarly, thinning of the inner nuclear layer was observed from 6 months in these rats. Immunohistochemical analysis showed decreased levels of rhodopsin and postsynaptic density protein-95 (PSD-95) proteins and increased levels of glial fibrillary acidic protein (GFAP), vascular endothelial growth factor (VEGF), and calretinin in WNIN/GR-Ob rats compared with the age-matched lean controls, further supporting cellular stress/damage and retinal degeneration. The retinal transcriptome analysis indicated altered expression profiles in both the WNIN/GR-Ob and WNIN/Ob rat models compared to their respective lean controls; these pathways are associated with activation of pathways like cellular oxidative stress response, inflammation, apoptosis, and phototransduction, although the changes were more prominent in WNIN/GR-Ob than in WNIN/Ob animals.ConclusionsWNIN/GR-Ob rats with added glucose intolerance developed retinal degeneration similar to the parent line WNIN/Ob. The severity of retinal degeneration was greater in WNIN/GR-Ob rats compared to WNIN/Ob, suggesting a possible role for IGT in this model. Hence, the WNIN/GR-Ob model could be a valuable tool for investigating the impact of MetS on retinal degeneration pathology