title: Combination of Mean Shift of Colour Signature and Optical Flow for Tracking During Foreground and Background Occlusion

authors: Hedayati, Mohammad

subjects: Object Tracking

abstract: This paper proposes a multiple hypothesis tracking for multiple object tracking with moving camera. The proposed model makes use of the stability of sparse optical flow along with the invariant colour property under size and pose variation, by merging the colour property of objects into optical flow tracking. To evaluate the algorithm five different videos are selected from broadcast horse races where each video represents different challenges that present in object tracking literature. A comparison study of the proposed method, with a colour based mean shift tracking proves the significant improvement in accuracy and stability of object tracking.

text: Combination of Mean Shift of Colour Signature and Optical Flow for Tracking During Foreground and Background Occlusion This paper proposes a multiple hypothesis tracking for multiple object tracking with moving camera. The proposed model makes use of the stability of sparse optical flow along with the invariant colour property under size and pose variation, by merging the colour property of objects into optical flow tracking. To evaluate the algorithm five different videos are selected from broadcast horse races where each video represents different challenges that present in object tracking literature. A comparison study of the proposed method, with a colour based mean shift tracking proves the significant improvement in accuracy and stability of object tracking.

year: 2015

title: High-throughput machine learning algorithms

authors: Mitchell, Rory

subjects: thesis with publication

abstract: The field of machine learning has become strongly compute driven, such that emerging research and applications require larger amounts of specialised hardware or smarter algorithms to advance beyond the state-of-the-art. This thesis develops specialised techniques and algorithms for a subset of computationally difficult machine learning problems. The applications under investigation are quantile approximation in the limited-memory data streaming setting, interpretability of decision tree ensembles, efficient sampling methods in the space of permutations, and the generation of large numbers of pseudorandom permutations. These specific applications are investigated as they represent significant bottlenecks in real-world machine learning pipelines, where improvements to throughput have significant impact on the outcomes of machine learning projects in both industry and research. To address these bottlenecks, we discuss both theoretical improvements, such as improved convergence rates, and hardware/software related improvements, such as optimised algorithm design for high throughput hardware accelerators. 

Some contributions include: the evaluation of bin-packing methods for efficiently scheduling small batches of dependent computations to GPU hardware execution units, numerically stable reduction operators for higher-order statistical moments, and memory bandwidth optimisation for GPU shuffling. Additionally, we apply theory of the symmetric group of permutations in reproducing kernel Hilbert spaces, resulting in improved analysis of Monte Carlo methods for Shapley value estimation and new, computationally more efficient algorithms based on kernel herding and Bayesian quadrature. We also utilise reproducing kernels over permutations to develop a novel statistical test for the hypothesis that a sample of permutations is drawn from a uniform distribution.

The techniques discussed lie at the intersection of machine learning, high-performance computing, and applied mathematics. Much of the above work resulted in open source software used in real applications, including the GPUTreeShap library [38], shuffling primitives for the Thrust parallel computing library [2], extensions to the Shap package [31], and extensions to the XGBoost library [6].

text: High-throughput machine learning algorithms The field of machine learning has become strongly compute driven, such that emerging research and applications require larger amounts of specialised hardware or smarter algorithms to advance beyond the state-of-the-art. This thesis develops specialised techniques and algorithms for a subset of computationally difficult machine learning problems. The applications under investigation are quantile approximation in the limited-memory data streaming setting, interpretability of decision tree ensembles, efficient sampling methods in the space of permutations, and the generation of large numbers of pseudorandom permutations. These specific applications are investigated as they represent significant bottlenecks in real-world machine learning pipelines, where improvements to throughput have significant impact on the outcomes of machine learning projects in both industry and research. To address these bottlenecks, we discuss both theoretical improvements, such as improved convergence rates, and hardware/software related improvements, such as optimised algorithm design for high throughput hardware accelerators. 

Some contributions include: the evaluation of bin-packing methods for efficiently scheduling small batches of dependent computations to GPU hardware execution units, numerically stable reduction operators for higher-order statistical moments, and memory bandwidth optimisation for GPU shuffling. Additionally, we apply theory of the symmetric group of permutations in reproducing kernel Hilbert spaces, resulting in improved analysis of Monte Carlo methods for Shapley value estimation and new, computationally more efficient algorithms based on kernel herding and Bayesian quadrature. We also utilise reproducing kernels over permutations to develop a novel statistical test for the hypothesis that a sample of permutations is drawn from a uniform distribution.

The techniques discussed lie at the intersection of machine learning, high-performance computing, and applied mathematics. Much of the above work resulted in open source software used in real applications, including the GPUTreeShap library [38], shuffling primitives for the Thrust parallel computing library [2], extensions to the Shap package [31], and extensions to the XGBoost library [6].

year: 2021

title: The 2020 cannabis referendum: Māori voter support, racialized policing, and the Criminal Justice System

authors: Dempster, Craig

abstract: In the New Zealand 2020 cannabis referendum, 50.7% of all voters rejected the creation of a legally-regulated cannabis market and instead supported retaining the current prohibitionist policy. Although the referendum failed to pass, a majority of Māori voted in favor of cannabis law reform. This paper suggests that within the Māori community there is a more nuanced appreciation of the impact of policing cannabis. Māori perceive that greater harm is caused by the racialized policing of cannabis than by the usage of it. Following McCreanor, et al. (2014), this paper employs a thematic, content analysis of the New Zealand Herald’s coverage of the 2020 cannabis referendum to investigate the presence of race-based targeting/policing in discussions of the legislation. The results reveal that racial disparities emerged as secondary to framing both the impact of cannabis and the referendum as race-neutral and affecting everyone in society equally. This paper argues that the impact of the policing of this particular drug impacts Māori differently, wherein they bear the brunt of racialized policing. Thus, Māori possess a more sophisticated understanding that warrants consideration because it is inextricably linked to lived experiences of policing that differ from wider social narratives of policing and drug policy in New Zealand.

text: The 2020 cannabis referendum: Māori voter support, racialized policing, and the Criminal Justice System In the New Zealand 2020 cannabis referendum, 50.7% of all voters rejected the creation of a legally-regulated cannabis market and instead supported retaining the current prohibitionist policy. Although the referendum failed to pass, a majority of Māori voted in favor of cannabis law reform. This paper suggests that within the Māori community there is a more nuanced appreciation of the impact of policing cannabis. Māori perceive that greater harm is caused by the racialized policing of cannabis than by the usage of it. Following McCreanor, et al. (2014), this paper employs a thematic, content analysis of the New Zealand Herald’s coverage of the 2020 cannabis referendum to investigate the presence of race-based targeting/policing in discussions of the legislation. The results reveal that racial disparities emerged as secondary to framing both the impact of cannabis and the referendum as race-neutral and affecting everyone in society equally. This paper argues that the impact of the policing of this particular drug impacts Māori differently, wherein they bear the brunt of racialized policing. Thus, Māori possess a more sophisticated understanding that warrants consideration because it is inextricably linked to lived experiences of policing that differ from wider social narratives of policing and drug policy in New Zealand.

year: 2022