Dissertation Defense: Vishwanath Sindagi
Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.
Title: Single Image Based Crowd Counting Using Deep Learning
Abstract: Estimating count and density maps from crowd images has a wide range of applications such as video surveillance, traffic monitoring, public safety and urban planning. In addition, techniques developed for crowd counting can be applied to related tasks in other fields of study such as cell microscopy, vehicle counting and environmental survey. The task of crowd counting and density map estimation from a single image is a difficult problem since it suffers from multiple issues like occlusions, perspective changes, background clutter, non-uniform density, intra-scene and inter-scene variations in scale and perspective. These issues are further exacerbated in highly congested scenes. In order to overcome these challenges, we propose a variety of different deep learning architectures that specifically incorporate various aspects such as global/local context information, attention mechanisms, specialized iterative and multi-level multi-pathway fusion schemes for combining information from multiple layers in a deep network. Through extensive experimentations and evaluations on several crowd counting datasets, we demonstrate that the proposed networks achieve significant improvements over existing approaches.
We also recognize the need for large amounts of data for training the deep networks and their inability to generalize to new scenes and distributions. To overcome this challenge, we propose novel semi-supervised and weakly-supervised crowd counting techniques that effectively leverage large amounts of unlabeled/weakly-labeled data. In addition to developing techniques with ability to learn from limited labeled data, we also introduce a new large-scale crowd counting dataset which can be used to train considerably larger networks. The proposed data consists of 4,372 high resolution images with 1.51 million annotations. We made explicit efforts to ensure that the images are collected under a variety of diverse scenarios and environmental conditions. The dataset provides a richer set of annotations like dots, approximate bounding boxes, blur levels, etc.
- Vishal Patel, Department of Electrical and Computer Engineering
- Rama Chellappa, Department of Electrical and Computer Engineering
- Alan Yuille, Department of Computer Science