Suchita Bhinge

A data-driven solution for abandoned object detection: Advantages of multiple types of diversity

The automated detection of abandoned objects is a quickly developing and widely researched field in video processing with specific application to automated surveillance. In the recent years, a number of approaches have been proposed to automatically detect abandoned objects. However, these techniques require prior knowledge of certain properties of the object such as its shape and color, to classify the foreground objects as abandoned object. The performance of tracking-based approaches degrades in complex scenes, i.e., when the abandoned object is occluded or in the case of crowding. In this paper, we propose a data-driven approach based on independent component analysis (ICA) for object detection. We demonstrate the success of the proposed ICA-based methodology with examples of videos with complex scenarios. We also show that algorithm choice plays an important role in performance, in particular when multiple types of diversities are taken into account and demonstrate the importance of order selection.

IVA for abandoned object detection: Exploiting dependence across color channels

Automated detection of abandoned object (AO) is an important application in video surveillance for security purposes. Because of its importance, a number of techniques have been proposed to automatically detect abandoned objects in the past years. However, these techniques require prior knowledge on the properties of the object such as its shape and color, in order to classify foreground objects as abandoned object. In contrast, independent component analysis (ICA) does not require such prior knowledge. However, it can only model one dataset at a time, thus limiting its usage to monochrome frames. In this paper, we propose to use independent vector analysis (IVA), a recent extension of ICA to multivariate data that takes the dependence across multiple datasets into account while retaining the independence within each dataset. We present a new framework for AO detection using IVA and show that it provides successful performance in complicated scenarios, such as for videos with crowd, illumination change, and occlusion.

Non-orthogonal constrained independent vector analysis: Application to data fusion

The existence of complementary information across multiple sensors has driven the proliferation of multivariate datasets. Exploitation of this common information, while minimizing the assumptions imposed on the data has led to the popularity of data-driven methods. Independent vector analysis (IVA), in particular, provides a flexible and effective approach for the fusion of multivariate data. In many practical applications, important prior information about the data exists and incorporating this information into the IVA model is expected to yield improved separation performance. In this paper, we propose a general formulation for non-orthogonal constrained IVA (C-IVA) framework that can incorporate prior information about either the sources or the mixing coefficients into the IVA cost function. A powerful decoupling method is the major enabling factor in this task. We demonstrate the improved performance of C-IVA over the unconstrained IVA model using both simulated as well as real medical imaging data.

A graph theoretical approach for performance comparison of ICA for fMRI analysis

Due to its relatively few assumptions, independent component analysis (ICA) has become a widely-used tool for the analysis of functional magnetic resonance imaging (fMRI) data. In its application, Infomax, has been by far the most frequently used ICA algorithm, primarily because it is the first ICA algorithm applied to fMRI analysis. However, now there are a number of more flexible ICA algorithms, which can exploit multiple types of statistical properties of the signals with fewer assumptions. In this work, we investigate the performance of Infomax and two of the more recent ICA algorithms, entropy bound minimization (EBM) and entropy rate bound minimization (ERBM), on resting state fMRI data derived from a large number of patients with schizophrenia (SZs) and healthy controls (HCs). In order to overcome the difficulty of directly comparing the performances of different ICA algorithms on real fMRI data, we propose the use of graph theoretic (GT) metrics to assess the quality of an ICA decomposition by measuring an algorithms ability to capture the inherent differences between SZs and HCs. Our results show that ERBM, the algorithm which incorporates the greatest number of statistical properties of the signals, provides the best performance for fMRI analysis.

The role of diversity in data-driven analysis of multi-subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics

Data‐driven methods have been widely used in functional magnetic resonance imaging (fMRI) data analysis. They extract latent factors, generally, through the use of a simple generative model. Independent component analysis (ICA) and dictionary learning (DL) are two popular data‐driven methods that are based on two different forms of diversity—statistical properties of the data—statistical independence for ICA and sparsity for DL. Despite their popularity, the comparative advantage of emphasizing one property over another in the decomposition of fMRI data is not well understood. Such a comparison is made harder due to the differences in the modeling assumptions between ICA and DL, as well as within different ICA algorithms where each algorithm exploits a different form of diversity. In this paper, we propose the use of objective global measures, such as time course frequency power ratio, network connection summary, and graph theoretical metrics, to gain insight into the role that different types of diversity have on the analysis of fMRI data. Four ICA algorithms that account for different types of diversity and one DL algorithm are studied. We apply these algorithms to real fMRI data collected from patients with schizophrenia and healthy controls. Our results suggest that no one particular method has the best performance using all metrics, implying that the optimal method will change depending on the goal of the analysis. However, we note that in none of the scenarios we test the highly popular Infomax provides the best performance, demonstrating the cost of exploiting limited form of diversity.

Data-driven fusion of multi-camera video sequences: Application to abandoned object detection

Due to the potential for object occlusion in crowded areas, the use of multiple cameras for video surveillance has prevailed over the use of a single camera. This has motivated the development of a number of techniques to analyze such multi-camera video sequences. However, most of these techniques require a camera calibration step, which is cumbersome and must be done for every new configuration. Additionally, these techniques fail to exploit the complementary information across these multiple datasets. We propose a data-driven solution to the problem by making use of the inherent similarity of temporal signatures of objects across video sequences. We introduce an effective solution for the detection of abandoned objects using this inherent diversity based on the transposed independent vector analysis (tIVA) model. By taking advantage of the similarity across multiple cameras, the new technique does not require any calibration and thus can be readily applied to any camera configuration. We demonstrate the superior performance of our technique over the single camera-based method using the PETS 2006 dataset.

Estimation of common subspace order across multiple datasets: Application to multi-subject fMRI data

The success of many joint blind source separation techniques is dependent upon accurate estimation of the common signal subspace order across multiple datasets. This has stimulated the development of techniques to estimate the number of common signals across two datasets, in particular, a method that uses information theoretic criteria using the canonical correlation coefficients in the likelihood formulation and a method based upon a two stage procedure, principal component analysis and canonical correlation analysis. However, these methods are limited to two datasets. In this paper, we propose a method based on multiset canonical correlation analysis followed by knee point detection (MCCA-KPD) to estimate the common subspace order across more than two datasets. We present a detailed comparison of the order estimation methods using simulated examples as well as real functional magnetic resonance imaging data. We demonstrate the superior performance of MCCA-KPD in terms of estimating the true common subspace order across multiple datasets.

Power spectra constrained IVA for enhanced detection of SSVEP content

The detection of steady state visual evoked potentials (SSVEPs) has been identified as an effective solution for brain computer interface (BCI) systems as well as for neurocognitive investigations of visually related tasks. SSVEPs are induced at the same frequency as the visual stimuli and can be observed in the scalp-based recordings of electroencephalogram signals, though they are one component buried amongst the normal brain signals and complex noise. Variations in individual response latencies as well as the presence of multiple biological artifacts complicate the use of direct frequency analysis, thus making blind source separation methods, such as independent component (ICA) and independent vector analysis (IVA) desirable solutions. IVA is a recent extension of ICA that decomposes multiple datasets simultaneously and has been been shown to be capable of enhancing and improving the detection of SSVEPs by exploiting the complimentary information that exists across EEG channels. In this work, we present a novel extension of IVA which incorporates a priori information to constrain the power spectral density (PSD) of the source estimates, known as constrained PSD IVA (CP-IVA) and demonstrate its improved SSVEP detection performance as well as stability over standard IVA and temporally constrained IVA (C-IVA).