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Limitations with Measuring Performance of Techniques for Abnormality Localization in Surveillance Video and How to Overcome them? 16

Limitations with Measuring Performance of Techniques for Abn...

引用

10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Sharma, Manoj Kumar Sarcar, Sayan Sheet, Debdoot Biswas, Prabir Kumar Indian Inst Technol Kharagpur Kharagpur W Bengal India Kochi Univ Technol Kami Kochi Japan

ISBN: (纸本)9781450347532

Now a days video surveillance is becoming more popular due to global security concerns and with the increasing need for effective monitoring of public places. the key goal of video surveillance is to detect suspicious or abnormal behavior. Various efforts have been made to detect an abnormality in the video. Further to these advancements, there is a need for better techniques for evaluation of abnormality localization in video surveillance. Existing technique mainly uses forty percent overlap rule with ground-truth data, and does not considers the extra predicted region into the computation. Existing metrics have been found to be inaccurate when more than one region is present within the frame which may or may not be correctly localized or marked as abnormal. this work attempts to bridge these limitations in existing metrics. In this paper, we investigate three existing metrics and discuss their benefits and limitations for evaluating localization of abnormality in video. We further extend the existing work by introducing penalty functions and substantiate the validity of proposed metrics with a sufficient number of instances. the presented metric are validated on data (35 different situations) for which the overlap has been computed analytically.

关键词： Video surveillance Abnormality localization Evaluation technique

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MPMF: Multi-part Multi-feature Based Object Tracking 16

MPMF: Multi-part Multi-feature Based Object Tracking

引用

10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Bhargava, Neha Chaudhuri, Subhasis Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9781450347532

the objective of tracking is to determine the states of an object in video frames while maintaining appearance and motion consistency. In this paper, we propose a novel multi-part multi-feature (MPMF) based object tracking which falls in the category of part-based trackers. We represent a target by a set of fixed parts (not semantic as body parts such as limbs, face) and each part is represented by a set of features. the multi-part representation of the object aids in partial occlusion handling and the multi-feature based object description increases robustness of the target representation. Instead of considering all the features of the parts, we measure tracker's confidence for a candidate by utilizing only the strong features of the candidate. this ensures that weak features do not interfere in the decision making. We also present an automatic method for selecting this subset of appropriate features for each part. To increase the tracker's speed and to reduce the number of erroneous candidates, we do not search in the whole frame. We keep the size of search area adaptive that depends on the tracker's confidence for the predicted location of the object. Additionally, it is easy to integrate more parts and features to the proposed tracker. the results on various challenging videos from VOT dataset are encouraging. MPMF outperforms state-of-the-art trackers on some of the standard challenging videos.

关键词： tracking part based algorithm feature selection

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Towards Quantifying the Amount of Uncollected Garbage through image Analysis 16

Towards Quantifying the Amount of Uncollected Garbage throug...

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Suresh, Susheel Sharma, Tarun Prashanth, T. K. Subramaniam, V Sitaram, Dinkar Nirupama, M. PES Inst Technol Dept Comp Sci Bengaluru 560085 India

ISBN: (纸本)9781450347532

Civic authorities in many indian cities have a tough time in garbage collection and as a result there is a pile up of garbage in the cities. In order to manage the situation, it is first required to be able to quantify the issue. In this paper, we address the problem of quantification of garbage in a dump using a two step approach. In the first step, we build a mobile application that allows citizens to capture images of garbage and upload them to a server. In the second step, back-end performs analysis on these images to estimate the amount of garbage using computer vision techniques. Our approach to volume estimation uses multiple images of the same dump (provided by the mobile application) from different perspectives, segments the dump from the background, reconstructs a three dimensional view of the dump and then estimates its volume. Using our novel pipeline, our experiments indicate that with 8 different perspectives, we are able to achieve an accuracy of about 85 % for estimating the volume.

关键词： Automatic Segmentation and 3D Reconstruction Segmentation using Deep Learning Structure from Motion Volume Estimation of Garbage Dumps

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Intrinsic image Decomposition using Focal Stacks 16

Intrinsic Image Decomposition using Focal Stacks

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Saini, Saurabh Sakurikar, Parikshit Narayanan, P. J. IIIT Hyderabad Kohli Ctr Intelligent Syst Ctr Visual Informat Technol Hyderabad Telangana India

ISBN: (纸本)9781450347532

In this paper, we presents a novel method (RGBF-IID) for intrinsic image decomposition of a wild scene without any restrictions on the complexity, illumination or scale of the image. We use focal stacks of the scene as input. A focal stack captures a scene at varying focal distances. Since focus depends on distance to the object, this representation has information beyond an RGB image towards an RGBD image with depth. We call our representation an RGBF image to highlight this. We use a robust focus measure and generalized random walk algorithm to compute dense probability maps across the stack. these maps are used to de fine sparse local and global pixel neighbourhoods, adhering to the structure of the underlying 3D scene. We use these neighbourhood correspondences with standard chromaticity assumptions as constraints in an optimization system. We present our results on both indoor and outdoor scenes using manually captured stacks of random objects under natural as well as artificial lighting conditions. We also test our system on a larger dataset of synthetically generated focal stacks from NYUv2 and MPI Sintel datasets and show competitive performance against current state-of-the-art IID methods that use RGBD images. Our method provides a strong evidence for the potential of RGBF modality in place of RGBD in computer vision.

关键词： Intrinsic image decomposition focal stacks generalized random walk RGBF RGBF-IID

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Complementary Tracker's Fusion for Robust Visual Tracking 16

Complementary Tracker's Fusion for Robust Visual Tracking

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Kakanuru, Sumithra Rapuru, Madan Kumar Mishra, Deepak Gorthi, Sai Subrahmanyam Indian Inst Space Sci & Technol Dept Avion Thiruvananthapuram Kerala India

ISBN: (纸本)9781450347532

though visual object tracking algorithms are capable of handling various challenging scenarios individually, still none of them is robust enough to handle all the challenges simultaneously. this paper aims at proposing a novel robust tracking algorithm by elegantly fusing the frame level detection strategy of Tracking Learning & Detection (TLD) with systematic model update strategy of Kernelized Correlation Filter tracker (KCF). the motivation behind the selection of trackers is their complementary nature in handling tracking challenges. the proposed algorithm efficiently combines the two tracking algorithms based on conservative correspondence measure with strategic model updates, which takes advantages of both and outperforms them on their short-ends by the virtue of other. the proposed fusion approach is quite general and any complimentary tracker (not just KCF) can be fused with TLD to leverage the best performance. Extensive evaluation of the proposed method based on different metrics is carried out on the datasets ALOV300++, Online Tracking Benchmark (OTB) and Visual Object Tracking (VOT2015) and demonstrated its superiority in terms of robustness and success rate by comparing with state-of-theart trackers.

关键词： Visual tracking trackers fusion correspondence measure

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CRF based method for Curb Detection using semantic cues and stereo depth 16

CRF based method for Curb Detection using semantic cues and ...

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Sodhi, Danish Upadhyay, Sarthak Bhatt, Dhaivat Krishna, K. Madhava Swarup, Shanti IIIT Hyderabad KCIS RRC Hyderabad Andhra Pradesh India Uurmi Syst Hyderabad Andhra Pradesh India

ISBN: (纸本)9781450347532

Curb detection is a critical component of driver assistance and autonomous driving systems. In this paper, we present a discriminative approach to the problem of curb detection under diverse road conditions. We define curbs as the intersection of drivable and non-drivable area which are classified using dense Conditional random fields(CRF). In our method, we fuse output of a neural network used for pixelwise semantic segmentation with depth and color information from stereo cameras. CRF fuses the output of a deep model and height information available in stereo data and provides improved segmentation. Further we introduce temporal smoothness using a weighted average of Segnet output and output from a probabilistic voxel grid as our unary potential. Finally, we show improvements over the current state of the art neural networks. Our proposed method shows accurate results over large range of variations in curb curvature and appearance, without the need of retraining the model for the specific dataset.

关键词： Stereovision Conditional Random Field Deep learning Curbs

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Scalable Clustering and Applications 16

Scalable Clustering and Applications

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Shahid, K., I Chaudhury, Santanu Indian Inst Technol Delhi Dept Elect Engn Delhi India Inst Pilani Cent Elect Engn Res Pilani Rajasthan India

ISBN: (纸本)9781450347532

Laprge scale machine learning is becoming an active research area recently. Most of the existing clustering algorithms cannot handle big data due to its high time and space complexity. Among the clustering algorithms, eigen vector based clustering, such as Spectral clustering, shows very good accuracy, but it has cubic time complexity. there are various methods proposed to reduce the time and space complexity for eigen decomposition such as Nystrom method, Lanczos method etc. Nystrom method has linear time complexity in terms of number of data points, but has cubic time complexity in terms of number of sampling points. To reduce this, various Rank k approximation methods also proposed, but which are less efficient compare to the normalized spectral clustering. In this paper we propose a two step algorithm for spectral clustering to reduce the time complexity to O (nmk + m(2) k'), by combining both Nystrom and Lanczos method, where k is the number of clusters and k' is the rank k approximation of the sampling matrix (k < k' << m << n). It shows very good results, with various data sets, image segmentation problems and churn prediction of a telecommunication data set, even with very low sampling (for 10 Million x 10 Million matrix, sampled only 100 columns) with lesser time, which confirms the validity of the algorithm.

关键词： Large scale machine learning Segmentation and grouping

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Local Dominant Binary Patterns for Recognition of Multi-view Facial Expressions 16

Local Dominant Binary Patterns for Recognition of Multi-view...

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Santra, Bikash Mukherjee, Dipti Prasad Indian Stat Inst 203 BT Rd Kolkata 700108 India

ISBN: (纸本)9781450347532

In this paper, a novel framework is proposed for automatic recognition of facial expressions. However, the face images for the proposed problem are captured at multiple view angle (i.e., multi-view facial expressions). the proposed scheme introduces a local dominant binary pattern (LDBP). Unlike uniform LBP based features, the LDBP uses fewer feature dimension without affecting the recognition performances. the LDBP is computed by improvising LBP with dominant orientations of neighborhood pixels. the eigen-value analysis of structure tensor representation of expressive face images determines the dominant directions of gray value changes in local neighbors of pixels. We use SVM for view-specific classification of multi-view facial expressions. the proposed model is experimented with the benchmark datasets of both near-frontal (CK+ and JAFEE) and multiview (KDEF, SFEW and LFPW) face images. the datasets include faces from posed as well as spontaneous expressions. the proposed scheme outperforms state-of-the-arts by approximately 1% for the near-frontal facial expressions and by at least 3% for multi-view facial expressions on an average.

关键词： Facial expression recognition Multi-view facial expression Local Binary Patterns (LBP) Local Dominant Binary Patterns (LDBP) structure tensor

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User guided generation of corroded objects 16

User guided generation of corroded objects

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Jain, Nisha Ranjan, Rohit Kalra, Prem Kumar, Subodh Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9781450347532

Rendering of corrosion often requires pain-staking modeling and texturing. On the other hand, there exist techniques for stochastic modeling of corrosion, which can automatically perform simulation and rendering under control of some user-specified parameters. Unfortunately, these parameters are non-intuitive and have a global impact. It is hard to determine the values of these parameters to obtain a desired look. For example, in real life corrosion gets influenced by both internal object-specific geometric factors, like sharp corners and curvatures, and external interventions like scratches, blemishes etc. Further, a graphics designer may want to selectively corrode areas to obtain a particular scene. We present a technique for user guided spread of corrosion. Our framework encapsulates both structural and aesthetic factors. Given the material properties and the surrounding environmental conditions of an object, we employ a physio-chemically based stochastic model to deduce the decay of different points on that object. Our system equips the user with a platform where the imperfections can be provided by either manual or systematic interference on a rendering of the three dimensional object. We demonstrate several user guided characteristic simulations encompassing varied influences including material, object characteristics and environment conditions. Our results are visually validated to understand the impact of imperfections with elapsed time.

关键词： Corrosion Simulation Rendering User guided Curvatures

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A Differential Excitation Based Rotational Invariance for Convolutional Neural Networks 16

A Differential Excitation Based Rotational Invariance for Co...

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Kandi, Haribabu Mishra, Deepak Subrahmanyam, G. R. K. S. Indian Inst Space Sci & Technol Dept Avion Trivandrum 695547 Kerala India

ISBN: (纸本)9781450347532

Deep Learning (DL) methods extract complex set of features using architectures containing hierarchical set of layers. the features so learned have high discriminative power and thus represents the input to the network in the most efficient manner. Convolutional Neural Networks (CNN) are one of the deep learning architectures, extracts structural features with little invariance to smaller translational, scaling and other forms of distortions. In this paper, the learning capabilities of CNN's are explored towards providing improvement in rotational invariance to its architecture. We propose a new CNN architecture with an additional layer formed by differential excitation against distance for the improvement of rotational invariance and is called as RICNN. Moreover, we show that the proposed method is giving superior performance towards invariance to rotations against the original CNN architecture (training samples with different orientations are not considered) without disturbing the invariance to smaller translational, scaling and other forms of distortions. Different profiles like training time, testing time and accuracies are evaluated at different percentages of training data for comparing the performance of the proposed configuration with original configuration.

关键词： Differential Excitation Rotational Invariance Deep Learning Convolutional Neural Networks Back Propagation

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