Automatic object segmentation of unstructured scenes using colour and depth maps

This study presents a segmentation pipeline that fuses colour and depth information to automatically separate objects of interest in video sequences captured from a quadcopter. Many approaches assume that cameras are static with known position, a condition which cannot be preserved in most outdoor robotic applications. In this study, the authors compute depth information and camera positions from a monocular video sequence using structure from motion and use this information as an additional cue to colour for accurate segmentation. The authors model the problem similarly to standard segmentation routines as a Markov random field and perform the segmentation using graph cuts optimisation. Manual intervention is minimised and is only required to determine pixel seeds in the first frame which are then automatically reprojected into the remaining frames of the sequence. The authors also describe an automated method to adjust the relative weights for colour and depth according to their discriminative properties in each frame. Experimental results are presented for two video sequences captured using a quadcopter. The quality of the segmentation is compared to a ground truth and other state-of-the-art methods with consistently accurate results.

Choroidal thickness in myopic and non-myopic children assessed with enhanced depth imaging optical coherence tomography

Purpose To examine choroidal thickness (ChT) and its topographical variation across the posterior pole in myopic and non-myopic children. Methods One hundred and four children aged 10-15 years of age (mean age 13.1 ± 1.4 years) had ChT measured using enhanced depth imaging optical coherence tomography (OCT). Forty one children were myopic (mean spherical equivalent -2.4 ± 1.5 D) and 63 non-myopic (mean +0.3 ± 0.3 D). Two series of 6 radial OCT line scans centred on the fovea were assessed for each child. Subfoveal ChT and ChT across a series of parafoveal zones over the central 6mm of the posterior pole were determined through manual image segmentation. Results Subfoveal ChT was significantly thinner in myopes (mean 303 ± 79 µm) compared to non-myopes (mean 359 ± 77 µm) (p<0.0001). Multiple regression analysis revealed both refractive error (r = 0.39, p<0.001) and age (r = 0.21, p = 0.02) were positively associated with subfoveal ChT. ChT also exhibited significant topographical variations, with the choroid being thicker in more central regions. The thinnest choroid was typically observed in nasal (mean 286 ± 77 µm) and inferior-nasal (306 ± 79 µm) locations, and the thickest in superior (346 ± 79 µm) and superior-temporal (341 ± 74 µm) locations. The difference in ChT between myopic and non-myopic children was significantly greater in central foveal regions compared to more peripheral regions (>3 mm diameter) (p<0.001). Conclusions Myopic children have significantly thinner choroids compared to non-myopic children of similar age, particularly in central foveal regions. The magnitude of difference in choroidal thickness associated with myopia appears greater than would be predicted by a simple passive choroidal thinning with axial elongation.

Automatic image scaling for place recognition in changing environments

Robustness to variations in environmental conditions and camera viewpoint is essential for long-term place recognition, navigation and SLAM. Existing systems typically solve either of these problems, but invariance to both remains a challenge. This paper presents a training-free approach to lateral viewpoint- and condition-invariant, vision-based place recognition. Our successive frame patch-tracking technique infers average scene depth along traverses and automatically rescales views of the same place at different depths to increase their similarity. We combine our system with the condition-invariant SMART algorithm and demonstrate place recognition between day and night, across entire 4-lane-plus-median-strip roads, where current algorithms fail.

Combat sports analytics: Boxing punch classification using overhead depth imagery

In competitive combat sporting environments like boxing, the statistics on a boxer's performance, including the amount and type of punches thrown, provide a valuable source of data and feedback which is routinely used for coaching and performance improvement purposes. This paper presents a robust framework for the automatic classification of a boxer's punches. Overhead depth imagery is employed to alleviate challenges associated with occlusions, and robust body-part tracking is developed for the noisy time-of-flight sensors. Punch recognition is addressed through both a multi-class SVM and Random Forest classifiers. A coarse-to-fine hierarchical SVM classifier is presented based on prior knowledge of boxing punches. This framework has been applied to shadow boxing image sequences taken at the Australian Institute of Sport with 8 elite boxers. Results demonstrate the effectiveness of the proposed approach, with the hierarchical SVM classifier yielding a 96% accuracy, signifying its suitability for analysing athletes punches in boxing bouts.

Tracking people in 3D using position, size and shape

This paper presents a prototype tracking system for tracking people in enclosed indoor environments where there is a high rate of occlusions. The system uses a stereo camera for acquisition, and is capable of disambiguating occlusions using a combination of depth map analysis, a two step ellipse fitting people detection process, the use of motion models and Kalman filters and a novel fit metric, based on computationally simple object statistics. Testing shows that our fit metric outperforms commonly used position based metrics and histogram based metrics, resulting in more accurate tracking of people.