Deformable Object Tracking with Gated Fusion

The tracking-by-detection framework receives growing attentions through the integration with the Convolutional Neural Networks (CNNs). Existing tracking-by-detection based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. Extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against state-of-the-art methods

Multi-object Tracking from the Classics to the Modern

Visual object tracking is one of the computer vision problems that has been researched extensively over the past several decades. Many computer vision applications, such as robotics, autonomous driving, and video surveillance, require the capability to track multiple objects in videos. The most popular solution approach to tracking multiple objects follows the tracking-by-detection paradigm in which the problem of tracking is divided into object detection and data association. In data association, track proposals are often generated by extending the object tracks from the previous frame with new detections in the current frame. The association algorithm then utilizes a track scorer or classifier in evaluating track proposals in order to estimate the correspondence between the object detections and object tracks. The goal of this dissertation is to design a track scorer and classifier that accurately evaluates track proposals that are generated during the association step. In this dissertation, I present novel track scorers and track classifiers that make a prediction based on long-term object motion and appearance cues and demonstrate its effectiveness in tracking by utilizing them within existing data association frameworks. First, I present an online learning algorithm that can efficiently train a track scorer based on a long-term appearance model for the classical Multiple Hypothesis Tracking (MHT) framework. I show that the classical MHT framework achieves competitive tracking performance even in modern tracking settings in which strong object detector and strong appearance models are available. Second, I present a novel Bilinear LSTM model as a deep, long-term appearance model which is a basis for an end-to-end learned track classifier. The architectural design of Bilinear LSTM is inspired by insights drawn from the classical recursive least squares framework. I incorporate this track classifier into the classical MHT framework in order to demonstrate its effectiveness in object tracking. Third, I present a novel multi-track pooling module that enables the Bilinear LSTM-based track classifier to simultaneously consider all the objects in the scene in order to better handle appearance ambiguities between different objects. I utilize this track classifier in a simple, greedy data association algorithm and achieve real-time, state-of-the-art tracking performance. I evaluate the proposed methods in this dissertation on public multi-object tracking datasets that capture challenging object tracking scenarios in urban areas.Ph.D

A Unifying Framework of Bilinear LSTMs

This paper presents a novel unifying framework of bilinear LSTMs that can represent and utilize the nonlinear interaction of the input features present in sequence datasets for achieving superior performance over a linear LSTM and yet not incur more parameters to be learned. To realize this, our unifying framework allows the expressivity of the linear vs. bilinear terms to be balanced by correspondingly trading off between the hidden state vector size vs. approximation quality of the weight matrix in the bilinear term so as to optimize the performance of our bilinear LSTM, while not incurring more parameters to be learned. We empirically evaluate the performance of our bilinear LSTM in several language-based sequence learning tasks to demonstrate its general applicability