Search Results for author: Andrew Wallace

Found 6 papers, 2 papers with code

RADIATE: A Radar Dataset for Automotive Perception in Bad Weather

1 code implementation18 Oct 2020 Marcel Sheeny, Emanuele De Pellegrin, Saptarshi Mukherjee, Alireza Ahrabian, Sen Wang, Andrew Wallace

To the best of our knowledge, this is the first public radar dataset which provides high-resolution radar images on public roads with a large amount of road actors labelled.

Autonomous Driving Benchmarking +4

300 GHz Radar Object Recognition based on Deep Neural Networks and Transfer Learning

no code implementations6 Dec 2019 Marcel Sheeny, Andrew Wallace, Sen Wang

For high resolution scene mapping and object recognition, optical technologies such as cameras and LiDAR are the sensors of choice.

Object Object Recognition +1

POL-LWIR Vehicle Detection: Convolutional Neural Networks Meet Polarised Infrared Sensors

no code implementations7 Apr 2018 Marcel Sheeny, Andrew Wallace, Mehryar Emambakhsh, Sen Wang, Barry Connor

For vehicle autonomy, driver assistance and situational awareness, it is necessary to operate at day and night, and in all weather conditions.

Long-term Correlation Tracking using Multi-layer Hybrid Features in Sparse and Dense Environments

no code implementations31 May 2017 Nathanael L. Baisa, Deepayan Bhowmik, Andrew Wallace

In this paper, we propose a new long-term visual tracking algorithm, learning discriminative correlation filters and using an online classifier, to track a target of interest in both sparse and crowded video sequences.

Visual Tracking

Development of a N-type GM-PHD Filter for Multiple Target, Multiple Type Visual Tracking

no code implementations31 May 2017 Nathanael L. Baisa, Andrew Wallace

We propose a new framework that extends the standard Probability Hypothesis Density (PHD) filter for multiple targets having $N\geq2$ different types based on Random Finite Set theory, taking into account not only background clutter, but also confusions among detections of different target types, which are in general different in character from background clutter.

Visual Tracking Vocal Bursts Type Prediction

Multiple Target, Multiple Type Filtering in RFS Framework

1 code implementation12 May 2017 Nathanael L. Baisa, Andrew Wallace

First, we extend the standard Probability Hypothesis Density (PHD) filter for multiple type of targets, each with distinct detection properties, to develop multiple target, multiple type filtering, N-type PHD filter, where $N\geq2$, for handling confusions among target types.

Applications

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