We focus on the challenging problem of efficient mouse 3D pose estimation based on static images, and especially single depth images. We introduce an approach to discriminatively train the split nodes of trees in random forest to improve their performance on estimation of 3D joint positions of mouse. Our algorithm is capable of working with different types of rodents and with different types of depth cameras and imaging setups. In particular, it is demonstrated in this paper that when a top-mounted depth camera is combined with a bottom-mounted color camera, the final system is capable of delivering full-body pose estimation including four limbs and the paws. Empirical examinations on synthesized and real-world depth images confirm the applicability of our approach on mouse pose estimation, as well as the closely related task of part-based labeling of mouse.
There has been substantial progress in mouse tracking using color cameras. Many commercial software used for studying social interaction of mice, such as HomeCageScan, EthoVision, AnyMaze, PhenoTracker and MiceProfiler @cite_14 use this technique to track up to two mice from color videos. A common issue with tracking is that it is highly sensitive to small color or texture perturbations over time. This often gives rise to failures, which makes a manual re-initialization of the system necessary and inevitable if used to track for long durations. There has been some recent progress in long-duration tracking, such as @cite_12 @cite_7 @cite_18 , which can work robustly without such manual intervention. This is typically achieved with the help of invasive methods such as attaching RFID chips or applying dedicated fur dyes to maintain identities reliably over time.
@cite_18 @cite_14 @cite_7 @cite_12
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Color-based trackers recently proposed in [3,4,5] have been proved robust and versatile for a modest computational cost. They are especially appealing for tracking tasks where the spatial structure of the tracked objects exhibits such a dramatic variability that trackers based on a space-dependent appearance reference would break down very fast. Trackers in [3,4,5] rely on the deterministic search of a window whose color content matches a reference histogram color model.Relying on the same principle of color histogram distance, but within a probabilistic framework, we introduce a new Monte Carlo tracking technique. The use of a particle filter allows us to better handle color clutter in the background, as well as complete occlusion of the tracked entities over a few frames.This probabilistic approach is very flexible and can be extended in a number of useful ways. In particular, we introduce the following ingredients: multi-part color modeling to capture a rough spatial layout ignored by global histograms, incorporation of a background color model when relevant, and extension to multiple objects. To track targets across networked cameras with disjoint views, one of the major problems is to learn the spatio-temporal relationship and the appearance relationship, where the appearance relationship is usually modeled as a brightness transfer function. Traditional methods learning the relationships by using either hand-labeled correspondence or batch-learning procedure are applicable when the environment remains unchanged. However, in many situations such as lighting changes, the environment varies seriously and hence traditional methods fail to work. In this paper, we propose an unsupervised method which learns adaptively and can be applied to long-term monitoring. Furthermore, we propose a method that can avoid weak links and discover the true valid links among the entry exit zones of cameras from the correspondence. Experimental results demonstrate that our method outperforms existing methods in learning both the spatio-temporal and the appearance relationship, and can achieve high tracking accuracy in both indoor and outdoor environment. We describe software, MiceProfiler, for automatic tracking of two interacting mice and analysis of their social interactions without the need of animal tagging. The program allows the identification of key elements that trigger social contact in different mouse strains. We use large amounts of unlabeled video to learn models for visual tracking without manual human supervision. We leverage the natural temporal coherency of color to create a model that learns to colorize gray-scale videos by copying colors from a reference frame. Quantitative and qualitative experiments suggest that this task causes the model to automatically learn to track visual regions. Although the model is trained without any ground-truth labels, our method learns to track well enough to outperform the latest methods based on optical flow. Moreover, our results suggest that failures to track are correlated with failures to colorize, indicating that advancing video colorization may further improve self-supervised visual tracking.