Fast and robust appearance-based tracking

We introduce a fast and robust subspace-based approach to appearance-based object tracking. The core of our approach is based on Fast Robust Correlation (FRC), a recently proposed technique for the robust estimation of large translational displacements. We show how the basic principles of FRC can be naturally extended to formulate a robust version of Principal Component Analysis (PCA) which can be efficiently implemented incrementally and therefore is particularly suitable for robust real-time appearance-based object tracking. Our experimental results demonstrate that the proposed approach outperforms other state-of-the-art holistic appearance-based trackers on several popular video sequences

Robust Outdoor Vehicle Visual Tracking Based on k-Sparse Stacked Denoising Auto-Encoder

Robust visual tracking for outdoor vehicle is still a challenging problem due to large object appearance variations caused by illumination variation, occlusion, and fast motion. In this chapter, k-sparse constraint is added to the encoder part of stacked auto-encoder network to learn more invariant feature of object appearance, and a stacked k-sparse-auto-encoder–based robust outdoor vehicle tracking method under particle filter inference is further proposed to solve the problem of appearance variance during the tracking. Firstly, a stacked denoising auto-encoder is pre-trained to learn the generic feature representation. Then, a k-sparse constraint is added to the stacked denoising auto-encoder, and the encoder of k-sparse stacked denoising auto-encoder is connected with a classification layer to construct a classification neural network. Finally, confidence of each particle is computed by the classification neural network and is used for online tracking under particle filter framework. Comprehensive tracking experiments are conducted on a challenging single-object tracking benchmark. Experimental results show that our tracker outperforms most state-of-the-art trackers

A hierarchical strategy for real-time tracking on-board UAVs

In this paper, we present a real-time tracking strategy based on direct methods for tracking tasks on-board UAVs, that is able to overcome problems posed by the challenging conditions of the task: e.g. constant vibrations, fast 3D changes, and limited capacity on-board. The vast majority of approaches make use of feature-based methods to track objects. Nonetheless, in this paper we show that although some of these feature-based solutions are faster, direct methods can be more robust under fast 3D motions (fast changes in position), some changes in appearance, constant vibrations (without requiring any specific hardware or software for video stabilization), and situations where part of the object to track is out the field of view of the camera. The performance of the proposed strategy is evaluated with images from real-flight tests using different evaluation mechanisms (e.g. accurate position estimation using a Vicon sytem). Results show that our tracking strategy performs better than well known feature-based algorithms and well known configurations of direct methods, and that the recovered data is robust enough for vision-in-the-loop tasks

Extracting Axial Depth and Trajectory Trend Using Astigmatism, Gaussian Fitting, and CNNs for Protein Tracking

Accurate analysis of vesicle trafficking in live cells is challenging for a number of reasons: varying appearance, complex protein movement patterns, and imaging conditions. To allow fast image acquisition, we study how employing an astigmatism can be utilized for obtaining additional information that could make tracking more robust. We present two approaches for measuring the z position of individual vesicles. Firstly, Gaussian curve fitting with CNN-based denoising is applied to infer the absolute depth around the focal plane of each localized protein. We demonstrate that adding denoising yields more accurate estimation of depth while preserving the overall structure of the localized proteins. Secondly, we investigate if we can predict using a custom CNN architecture the axial trajectory trend. We demonstrate that this method performs well on calibration beads data without the need for denoising. By incorporating the obtained depth information into a trajectory analysis, we demonstrate the potential improvement in vesicle tracking