Avideh Zakhor

Adaptive Long-Distance Navigation for Autonomous Drones

Abstract

This project leverages a Deep Reinforcement Learning (DRL) approach to enable a large drone to navigate toward goal positions in unknown outdoor settings while avoiding obstacles. Utilizing state information and depth imagery, our method uniquely integrates pre-computed optimal trajectories—determined during privileged learning phases—as a supervisory signal with the exploratory benefits of an RL agent.

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Automated Collision Prediction in Autonomous Systems with Monocular Camera

This project aims to improve real world, wide field of view depth estimation using monocular sensors. In doing so, various geometry of indoor and outdoor sceneries will be experimented with using large deep learning models. A focus will be placed on data representation in the process in order to investigate and identify the most efficient pipelines.

Researchers Jerome Quenum, University of California - Berkeley Brent Yi, University of California - Berkeley Avideh Zakhor, University of California - Berkeley Austin Stone, Google Rico Jonschkowski,...

Video Representation Learning for Global and Local Features

This ongoing project attempts to use self-supervised learning to learn video representation that are both useful for coarser tasks involving global informationand finer-grained tasks involving local information.

Researchers Franklin Wang, UC Berkeley Avideh Zakhor, UC Berkeley Yale Song, Microsoft Du Tran, Facebook Aravind Kalaiah, Facebook Overview

Self-supervised video representation learning provides new opportunities to computer vision: It can take full advantage of the wealth of unlabeled video data available, and when successful, it can improve...

Barcode Image Denoise - RL Approach

Barcode reading has various applications in warehouses and as such is critical to supply chain automation. In this project, we develop a pipeline for decoding 1D and 2D segmented barcode images. Our pipeline consists of two parts: denoising network and angle correction. The denoising network deals with removing noise so as to improve the decoding rate of barcodes using open source software such as Zerbra. Angle correction has to do with applying rotation algorithms to segmented barcode images without sacrificing their spatial resolution and introducing anti-aliasing. ...

Efficient Object Detection with Super High-Resolution Image

Object detection in Ultra High-Resolution (UHR) images has long been a challenging problem in computer vision due to the varying scales of the targeted objects. When it comes to barcode detection, resizing UHR input images to smaller sizes often leads to the loss of pertinent information, while processing them directly is highly in-...

Deep Image Processing

More information on this project coming soon, please check back.