The problem of tracking multiple objects in a video sequence poses several challenging tasks. For tracking-bydetection, these include object re-identification, motion prediction and dealing with occlusions. We present a tracker (without bells and whistles) that accomplishes tracking without specifically targeting any of these tasks, in particular, we perform no training or optimization on tracking data. To this end, we exploit the bounding box regression of an object detector to predict the position of an object in the next frame, thereby converting a detector into a Tracktor. We demonstrate the potential of Tracktor and provide a new state-of-the-art on three multi-object tracking benchmarks by extending it with a straightforward re-identification and camera motion compensation.We then perform an analysis on the performance and failure cases of several state-of-the-art tracking methods in comparison to our Tracktor. Surprisingly, none of the dedicated tracking methods are considerably better in dealing with complex tracking scenarios, namely, small and occluded objects or missing detections. However, our approach tackles most of the easy tracking scenarios. Therefore, we motivate our approach as a new tracking paradigm and point out promising future research directions. Overall, Tracktor yields superior tracking performance than any current tracking method and our analysis exposes remaining and unsolved tracking challenges to inspire future research directions.

Abstract:The common approach for the flow factor calculation is based on using the Reynolds equation to simulate the micro-level flow. However, for structured surfaces the fluid flow cannot be represented correctly, due to the assumptions made when deriving the Reynolds equation. In this work, a novel method using the Navier-Stokes equations for the calculation of the micro-level flow is presented and validated against results from Patir and Cheng. The three-dimensional lubrication gap was generated by a rough Gaussian random surface and a perfectly smooth moving counter surface, in order to be available for different numerical methods. The presented results illustrate similar trends for both the approaches. Additionally, the use of the Navier-Stokes equations allows for the observance of surface induced effects which cannot be resolved by the approach of Patir and Cheng. Furthermore, a numerical approach for a shear flow factor calculation with a rough moving surface is presented and validated against other simulation methods. While the validation is maintained with pressure-and temperature-independent density and viscosity, these effects will be taken into account for later research activities of textured surfaces.

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Hydrodynamic journal bearings are used within a wide range of machines, such as combustion engines, gas turbines, or wind turbines. For a safe operation, awareness of the lubrication regime, in which the bearing is currently operating, is of great importance. In the current study, highspeed data signals of a torque sensor, sampled with a frequency of 1000 hz in a time range of 2.5 s, obtained on a journal bearing test-rig under various operating conditions, are used to train machine learning models, such as neural networks and logistic regression. Results indicate that a fast Fourier transform (fft) of the highspeed torque signals enables accurate predictions of lubrication regimes. The trained models are analysed in order to identify distinctive frequencies for the respective lubrication regime.