Jawadul H. Bappy

Distributed Multi-Target Tracking and Data Association in Vision Networks

Distributed algorithms have recently gained immense popularity. With regards to computer vision applications, distributed multi-target tracking in a camera network is a fundamental problem. The goal is for all cameras to have accurate state estimates for all targets. Distributed estimation algorithms work by exchanging information between sensors that are communication neighbors. Vision-based distributed multi-target state estimation has at least two characteristics that distinguishes it from other applications. First, cameras are directional sensors and often neighboring sensors may not be sensing the same targets, i.e., they are naive with respect to that target. Second, in the presence of clutter and multiple targets, each camera must solve a data association problem. This paper presents an information-weighted, consensus-based, distributed multi-target tracking algorithm referred to as the Multi-target Information Consensus (MTIC) algorithm that is designed to address both the naivety and the data association problems. It converges to the centralized minimum mean square error estimate. The proposed MTIC algorithm and its extensions to non-linear camera models, termed as the Extended MTIC (EMTIC), are robust to false measurements and limited resources like power, bandwidth and the real-time operational requirements. Simulation and experimental analysis are provided to support the theoretical results.

Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning

Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries.

Online Adaptation for Joint Scene and Object Classification

Recent efforts in computer vision consider joint scene and object classification by exploiting mutual relationships (often termed as context) between them to achieve higher accuracy. On the other hand, there is also a lot of interest in online adaptation of recognition models as new data becomes available. In this paper, we address the problem of how models for joint scene and object classification can be learned online. A major motivation for this approach is to exploit the hierarchical relationships between scenes and objects, represented as a graphical model, in an active learning framework. To select the samples on the graph, which need to be labeled by a human, we use an information theoretic approach that reduces the joint entropy of scene and object variables. This leads to a significant reduction in the amount of manual labeling effort for similar or better performance when compared with a model trained with the full dataset. This is demonstrated through rigorous experimentation on three datasets.

CNN based region proposals for efficient object detection

In computer vision, object detection is addressed as one of the most challenging problems as it is prone to localization and classification error. The current best-performing detectors are based on the technique of finding region proposals in order to localize objects. Despite having very good performance, these techniques are computationally expensive due to having large number of proposed regions. In this paper, we develop a high-confidence region-based object detection framework that boosts up the classification performance with less computational burden. In order to formulate our framework, we consider a deep network that activates the semantically meaningful regions in order to localize objects. These activated regions are used as input to a convolutional neural network (CNN) to extract deep features. With these features, we train a set of class-specific binary classifiers to predict the object labels. Our new region-based detection technique significantly reduces the computational complexity and improves the performance in object detection. We perform rigorous experiments on PASCAL, SUN, MIT-67 Indoor and MSRC datasets to demonstrate that our proposed framework outperforms other state-of-the-art methods in recognizing objects.

Opportunistic Image Acquisition of Individual and Group Activities in a Distributed Camera Network

The decreasing cost and size of video sensors has led to camera networks becoming pervasive in our lives. However, the ability to analyze these images effectively is very much a function of the quality of the acquired images. In this paper, we consider the problem of automatically controlling the fields of view of individual pan–tilt–zoom (PTZ) cameras in a camera network leading to improved situation awareness (e.g., where and what are the critical targets and events) in a region of interest. The network of cameras attempts to observe the entire region of interest at some minimum resolution while opportunistically acquiring high resolution images of critical events in real time. Since many activities involve groups of people interacting, an important decision that the network needs to make is whether to focus on individuals or groups of them. This is achieved by understanding the performance of video analysis tasks and designing camera control strategies to improve a metric that quantifies the quality of the source imagery. Optimization strategies, along with a distributed implementation, are proposed, and their theoretical properties analyzed. The proposed methods bring together computer vision and network control ideas. The performance of the proposed methodologies discussed herein has been evaluated on a real-life wireless network of PTZ capable cameras.

Non-uniform Subset Selection for Active Learning in Structured Data

Several works have shown that relationships between data points (i.e., context) in structured data can be exploited to obtain better recognition performance. In this paper, we explore a different, but related, problem: how can these inter-relationships be used to efficiently learn and continuously update a recognition model, with minimal human labeling effort. Towards this goal, we propose an active learning framework to select an optimal subset of data points for manual labeling by exploiting the relationships between them. We construct a graph from the unlabeled data to represent the underlying structure, such that each node represents a data point, and edges represent the inter-relationships between them. Thereafter, considering the flow of beliefs in this graph, we choose those samples for labeling which minimize the joint entropy of the nodes of the graph. This results in significant reduction in manual labeling effort without compromising recognition performance. Our method chooses non-uniform number of samples from each batch of streaming data depending on its information content. Also, the submodular property of our objective function makes it computationally efficient to optimize. The proposed framework is demonstrated in various applications, including document analysis, scene-object recognition, and activity recognition.

Inter-dependent CNNs for joint scene and object recognition

In this paper, we consider two inter-dependent deep networks, where one network taps into the other, to perform two challenging cognitive vision tasks - scene classification and object recognition jointly. Recently, convolutional neural networks have shown promising results in each of these tasks. However, as scene and objects are interrelated, the performance of both of these recognition tasks can be further improved by exploiting dependencies between scene and object deep networks. The advantages of considering the inter-dependency between these networks are the following: 1. improvement of accuracy in both scene and object classification, and 2. significant reduction of computational cost in object detection. In order to formulate our framework, we employ two convolutional neural networks (CNNs), scene-CNN and object-CNN. We utilize scene-CNN to generate object proposals which indicate the probable object locations in an image. Object proposals found in the process are semantically relevant to the object. More importantly, the number of object proposals is fewer in amount when compared to other existing methods which reduces the computational cost significantly. Thereafter, in scene classification, we train three hidden layers in order to combine the global (image as a whole) and local features (object information in an image). Features extracted from CNN architecture along with the features processed from object-CNN are combined to perform efficient classification. We perform rigorous experiments on five datasets to demonstrate that our proposed framework outperforms other state-of-the-art methods in classifying scenes as well as recognizing objects.

The Impact of Typicality for Informative Representative Selection

In computer vision, selection of the most informative samples from a huge pool of training data in order to learn a good recognition model is an active research problem. Furthermore, it is also useful to reduce the annotation cost, as it is time consuming to annotate unlabeled samples. In this paper, motivated by the theories in data compression, we propose a novel sample selection strategy which exploits the concept of typicality from the domain of information theory. Typicality is a simple and powerful technique which can be applied to compress the training data to learn a good classification model. In this work, typicality is used to identify a subset of the most informative samples for labeling, which is then used to update the model using active learning. The proposed model can take advantage of the inter-relationships between data samples. Our approach leads to a significant reduction of manual labeling cost while achieving similar or better recognition performance compared to a model trained with entire training set. This is demonstrated through rigorous experimentation on five datasets.

Efficient selection of informative and diverse training samples with applications in scene classification

The huge amount of time required to construct a set of labeled images to train a classifier has led researchers to develop algorithms which can identify the most informative training images, such that labelling those will be sufficient to achieve a considerable classification accuracy. In this paper we focus on choosing a subset of the most informative and diverse images based on which the classification model can be learned efficiently. The size of the subset to be chosen is determined by the available budget for manual labeling. Although the problem of identifying the informative images can be solved by active learning algorithms, it will require a set of labeled images for initial model construction, which is not required in our method as we identify the best samples at one shot. We incorporate the concepts of strong and weak teacher to help the learner to learn the model efficiently with limited budget for manual labeling. We perform rigorous experiments on two challenging scene classification datasets to demonstrate the effectiveness of our algorithm.