Recently, privacy has a growing importance in several domains, especially in street-view images. The conventional way to achieve this is to automatically detect and blur sensitive information from these images. However, the processing cost of blurring increases with the ever-growing resolution of images. We propose a system that is cost-effective even after increasing the resolution by a factor of 2.5. The new system utilizes depth data obtained from LiDAR to significantly reduce the search space for detection, thereby reducing the processing cost. Besides this, we test several detectors after reducing the detection space and provide an alternative solution based on state-of-the-art deep learning detectors to the existing HoG-SVM-Deep system that is faster and has a higher performance.
Nowadays, LiDAR-based system are becoming more prevalent in the autonomous driving industry. RGB-D-based object detection has been explored in @cite_1 @cite_7 . However, to the best of our knowledge, this is the first work that utilizes depth obtained from LiDAR to reduce the search space of a detection algorithm. Plane fitting in point clouds using Random Sample Consensus (RANSAC) is well studied and several improvements have been proposed @cite_6 . In this research, we utilize depth data obtained from LiDAR to create a point cloud which is later used to estimate planes. The estimated planes are further applied to reduce the search space in the cycloramas.
@cite_1 @cite_6 @cite_7
2750980686 2085261163
Object recognition from RGB-D images has become a hot topic and gained a significant popularity in recent years due to its numerous applications. In this paper, we propose a novel multimodal deep convolutional neural networks architecture for RGB-D object recognition which composed of three streams with two different types of deep CNNs, where each stream can separately learn from each modality. Finally, we propose a combined architecture of joint network of these three streams to classify the objects. Compared to RGB data, RGB-D images provide additional depth information that can be represented as depth colorization methods or surface normals. Our goal is to exploit both colorization and surface normals information to encode depth images. We show that by utilizing both colorization and surface normals of depth images combined with RGB significantly can improves the classification accuracy. We evaluate our model on one of the most challenging RGB-D object dataset and achieves comparable performance to state-of-the-art methods. A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing