Thesis Proposal: Vishwanath Sindagi

When:
September 10, 2020 @ 3:00 pm
2020-09-10T15:00:00-04:00
2020-09-10T15:15:00-04:00
Thesis Proposal: 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 Techniques

Abstract: With ubiquitous usage of surveillance cameras and advances in computer vision, crowd scene analysis has gained a lot of interest in the recent years. In this work, we focus on the task of estimating crowd count and high-quality density maps which has wide applications in video surveillance, traffic monitoring, public safety, urban planning, scene understanding and flow monitoring. Also, the methods developed for crowd counting can be extended to counting tasks in other fields such as cell microscopy, vehicle counting, environmental survey, etc. The task of crowd counting and density estimation has seen significant progress in the recent years. However, due to the presence of various complexities such as occlusions, high clutter, non-uniform distribution of people, non-uniform illumination, intra-scene and inter-scene variations in appearance, scale and perspective, the resulting accuracies are far from optimal. Furthermore, existing methods tend to perform poorly on datasets that are different from the dataset used for training the models.

In this work, we specifically address two of the major issues plaguing the crowd counting community: (i) scale variations and (ii) poor cross-dataset performance. In order to address the problem of scale variations, we analyze existing scale-aware counting models and identify that their poor performance is due to the lack of contextual information and the poor quality of predicted density maps. We propose to overcome these issues by incorporating multiple context cues into the learning process, and additionally improving the quality of the predicted density maps using adversarial training. Finally, we explore the use of contextual information as weak image-level labels to improve cross-dataset performance.

Committee Members

Rama Chellappa, Department of Electrical and Computer Engineering

Carlos Castillo, Department of Electrical and Computer Engineering

Vishal Patel, Department of Electrical and Computer Engineering

 

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