Pixel-Level Deep Multi-Dimensional Embeddings for Homogeneous Multiple Object Tracking

The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, explicitly-defined post-processing steps. This work introduces two detection methods that incorporate multi-dimensional embeddings. We train deep CNNs to produce easily-clusterable embeddings for semantic instance segmentation and to enable object detection through pose estimation. The use of embeddings allows the method to identify per-pixel instance membership for both tasks. Our method specifically targets applications that require long-term tracking of homogeneous targets using a stationary camera. Furthermore, this method was developed and evaluated on a livestock tracking application which presents exceptional challenges that generalized tracking methods are not equipped to solve. This is largely because contemporary datasets for multiple object tracking lack properties that are specific to livestock environments. These include a high degree of visual similarity between targets, complex physical interactions, long-term inter-object occlusions, and a fixed-cardinality set of targets. For the reasons stated above, our method is developed and tested with the livestock application in mind and, specifically, group-housed pigs are evaluated in this work. Our method reliably detects pigs in a group housed environment based on the publicly available dataset with 99% precision and 95% using pose estimation and achieves 80% accuracy when using semantic instance segmentation at 50% IoU threshold. Results demonstrate our method\u27s ability to achieve consistent identification and tracking of group-housed livestock, even in cases where the targets are occluded and despite the fact that they lack uniquely identifying features. The pixel-level embeddings used by the proposed method are thoroughly evaluated in order to demonstrate their properties and behaviors when applied to real data. Adivser: Lance C. Pére

On-barn pig weight estimation based on body measurements by a Kinect v1 depth camera

Information on the daily growth rate of pigs enables the stockman to monitor their performance and health and to predict and control their market weight and date. Manual measurements are among the most common ways to get an indication of animal growth. However, this approach is laborious and di\ufb03cult, and it may be stressful for both the pigs and the stockman. As a consequence, manual measurements can be very time-consuming, induce costs and sometimes cause injuries to the animals and the stockman. The present work proposes the implementation of a Microsoft Kinect v1 depth camera for the fast, non-contact measurement of pig body dimensions such as heart girth, length and height. In the present work, these dimension values were related to animal weight, and two models (linear and non-linear) were developed and applied to the Kinect and manual measurement data. Both models were highly correlated with the direct weight measurements considered as references, as demonstrated by high coe\ufb03cients of determination (R2 > 0.95). Speci\ufb01cally, in the case of the non-linear model based on non-contact depth camera measurements, the mean absolute error exhibited a reduction of over 40% compared to the same non-linear model based on manual measurements (from 0.82 to 0.48 kg)