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This paper is about enabling robots to improve their perceptual performance through repeated use in their operating environment, creating local expert detectors fitted to the places through which a robot moves. We leverage the concept of ‘experiences’ in visual perception for robotics, accounting for bias in the data a robot sees by fitting object detector models to a particular ‘place'. The key question...
Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. While supervised learning optimises a model for the training domain, it will deliver degraded performance in application domains that underlie distributional shifts caused by these changes. Traditionally, this problem has been addressed...
This paper addresses the problem of detecting people and vehicles on a surface mine by presenting an architecture that combines the complementary strengths of deep convolutional networks (DCN) with cluster‐based analysis. We highlight that using a DCN in a naïve black box approach results in a significantly high rate of errors due to the lack of mining‐specific training data and the unique landscape...
This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tracking performance, where changing the detector can improve tracking by up to 18.9%. Despite only using a rudimentary combination of familiar techniques such as...
This paper presents a self-supervised approach for learning to associate object detections in a video sequence as often required in tracking-by-detection systems. In this paper we focus on learning an affinity model to estimate the data association cost, which can adapt to different situations by exploiting the sequential nature of video data. We also propose a framework for gathering additional training...
We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN). The fine-grained image classification problem is characterised by large intra-class variations and small inter-class variations. To overcome these problems our proposed MixDCNN system partitions images into K subsets of similar images and learns an expert DCNN...
We propose a novel method to improve fine-grained bird species classification based on hierarchical subset learning. We first form a similarity tree where classes with strong visual correlations are grouped into subsets. An expert local classifier with strong discriminative power to distinguish visually similar classes is then learnt for each subset. On the challenging Caltech200-2011 bird dataset...
This paper presents a method for the continuous segmentation of dynamic objects using only a vehicle mounted monocular camera without any prior knowledge of the object's appearance. Prior work in online static/dynamic segmentation [1] is extended to identify multiple instances of dynamic objects by introducing an unsupervised motion clustering step. These clusters are then used to update a multi-class...
This paper presents a method for measuring the in-bucket payload volume on a dragline excavator for the purpose of estimating the material's bulk density in real-time. Knowledge of the payload's bulk density can provide feedback to mine planning and scheduling to improve blasting and therefore provide a more uniform bulk density across the excavation site. This allows a single optimal bucket size...
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