Obstacle avoidance based-visual navigation for micro aerial vehicles

This paper describes an obstacle avoidance system for low-cost Unmanned Aerial Vehicles (UAVs) using vision as the principal source of information through the monocular onboard camera. For detecting obstacles, the proposed system compares the image obtained in real time from the UAV with a database of obstacles that must be avoided. In our proposal, we include the feature point detector Speeded Up Robust Features (SURF) for fast obstacle detection and a control law to avoid them. Furthermore, our research includes a path recovery algorithm. Our method is attractive for compact MAVs in which other sensors will not be implemented. The system was tested in real time on a Micro Aerial Vehicle (MAV), to detect and avoid obstacles in an unknown controlled environment; we compared our approach with related works.Peer ReviewedPostprint (published version

Visual Object Tracking Approach Based on Wavelet Transforms

In this Thesis, a new visual object tracking (VOT) approach is proposed to overcome the main challenging problem encountered within the existing approaches known as the significant appearance changes which is due mainly to the heavy occlusion and illumination variations. Indeed, the proposed approach is based on combining the deep convolutional neural networks (CNN), the histograms of oriented gradients (HOG) features, and the discrete wavelet packet transform to ensure the implementation of three ideas. Firstly, solving the problem of illumination variation by incorporating the coefficients of the image discrete wavelet packet transform instead of the image template to handle the case of images with high saturation in the input of the used CNN, whereas the inverse discrete wavelet packet transform is used at the output for extracting the CNN features. Secondly, by combining four learned correlation filters with convolutional features, the target location is deduced using multichannel correlation maps at the CNNs output. On the other side, the maximum value of the resulting maps from correlation filters with convolutional features produced by HOG feature of the image template previously obtained are calculated and which are used as an updating parameter of the correlation filters extracted from CNN and from HOG where the major aim is to ensure long-term memory of target appearance so that the target item may be recovered if tracking fails. Thirdly, to increase the performance of HOG, the coefficients of the discrete packet wavelet transform are employed instead of the image template. Finally, for the validation and the evaluation of the proposed tracking approach performance based on specific performance metrics in comparison to the state-of-the-art counterparts, extensive simulation experiments on benchmark datasets have been conducted out, such as OTB50, OTB100 , TC128 ,and UAV20. The obtained results clearly prove the validity of the proposed approach in solving the encountered problems of visual object tracking in almost the experiment cases presented in this thesis compared to other existing tracking approaches

Color Invariant SURF in Discriminative Object Tracking

Tracking can be seen as an online learning problem, where the focus is on discriminating object from background. From this point of view, features play a key role as the tracking accuracy depends on how well the feature distinguish object and background. Current discriminative trackers use traditional features such as intensity, RGB and full body shape features. In this paper, we propose to use color invariant SURF features in the discriminative tracking. This set of invariant features has been shown to be of increased invariance and discriminative power. The resulting tracker inherits a good discrimination between object and background while keeping advantages of the discriminative tracking framework. Experiments on a dataset of 80 videos covering a wide range of tracking circumstances show that the tracker is robust to changes in object appearance, lighting conditions and able to track objects under cluttered scenes and partial occlusion

Color Invariant SURF in Discriminative Object Tracking

Abstract. Tracking can be seen as an online learning problem, where the focus is on discriminating object from background. From this point of view, features play a key role as the tracking accuracy depends on how well the feature distinguish object and background. Current discriminative trackers use traditional features such as intensity, RGB and full body shape features. In this paper, we propose to use color invariant SURF features in the discriminative tracking. This set of invariant features has been shown to be of increased invariance and discriminative power. The resulting tracker inherits a good discrimination between object and background while keeping advantages of the discriminative tracking framework. Experiments on a dataset of 80 videos covering a wide range of tracking circumstances show that the tracker is robust to changes in object appearance, lighting conditions and able to track objects under cluttered scenes and partial occlusion. Key words: tracking, surf, color, invariant