LiDAR-assisted Large-scale Privacy Protection in Street-view Cycloramas

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.Comment: Accepted at Electronic Imaging 201

Aggregated Deep Local Features for Remote Sensing Image Retrieval

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g. 50% faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal contributio

Bootstrapped CNNs for Building Segmentation on RGB-D Aerial Imagery

Detection of buildings and other objects from aerial images has various applications in urban planning and map making. Automated building detection from aerial imagery is a challenging task, as it is prone to varying lighting conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are robust against some of these variations, although they fail to distinguish easy and difficult examples. We train a detection algorithm from RGB-D images to obtain a segmented mask by using the CNN architecture DenseNet.First, we improve the performance of the model by applying a statistical re-sampling technique called Bootstrapping and demonstrate that more informative examples are retained. Second, the proposed method outperforms the non-bootstrapped version by utilizing only one-sixth of the original training data and it obtains a precision-recall break-even of 95.10% on our aerial imagery dataset.Comment: Published at ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Science

Enabling Open-Set Person Re-Identification for Real-World Scenarios

Person re-identification (re-ID) is a significant problem of computer vision with increasing scientific attention. To date, numerous studies have been conducted to improve the accuracy and robustness of person re-ID to meet the practical demands. However, most of the previous efforts concentrated on solving the closed-set variant of the problem, where a query is assumed to always have a correct match within the set of known people (the gallery set). However, this assumption is usually not valid for the industrial re-ID use cases. In this study, we focus on the open-set person re-ID problem, where, in addition to the similarity ranking, the solution is expected to detect the presence or absence of a given query identity within the gallery set. To determine good practices and to assess the practicality of the person re-ID in industrial applications, first, we convert popular closed-set person re-ID datasets into the open-set scenario. Second, we compare performance of eight state-of-the-art closed-set person re-ID methods under the open-set conditions. Third, we experimentally determine the efficiency of using different loss function combinations for the open-set problem. Finally, we investigate the impact of a statistics-driven gallery refinement approach on the open-set person re-ID performance in the low false-acceptance rate (FAR) region, while simultaneously reducing the computational demands of retrieval. Results show an average detection and identification rate increase of 8.38% and 3.39% on the DukeMTMC-reID and Market1501 datasets, respectively, for a FAR of 1%

Cascaded CNN method for far object detection in outdoor surveillance

In maritime surveillance, detection of small ships and vessels located far away in the scene is of vital importance for behaviour analysis. Comparing to closely located objects, far objects are often captured in a smaller size and lack the adequate amount of details. Therefore, conventional detectors fail to recognize them. This paper proposes a CNN-based cascaded method for reliable detection of objects and more specifically vessels, located far away from a surveillance camera. The cascaded method improves small object detection accuracy by additional processing of the obtained candidate regions in their original resolution. The additional processing includes another detection iteration and a sequence of detection verification steps. Experimental results on our real-world vessel evaluation dataset reveal that the cascaded method increases the recall rate and F1- measurement by 13% and 12%, respectively. Another benefit is that the method does not require an adopter to change the model and architecture of the applied network. As an additional contribution, we provide a labeled maritime dataset to open public access.</p

Homography Estimation in Complex Topological Scenes

Surveillance videos and images are used for a broad set of applications, ranging from traffic analysis to crime detection. Extrinsic camera calibration data is important for most analysis applications. However, security cameras are susceptible to environmental conditions and small camera movements, resulting in a need for an automated re-calibration method that can account for these varying conditions. In this paper, we present an automated camera-calibration process leveraging a dictionary-based approach that does not require prior knowledge on any camera settings. The method consists of a custom implementation of a Spatial Transformer Network (STN) and a novel topological loss function. Experiments reveal that the proposed method improves the IoU metric by up to 12% w.r.t. a state-of-the-art model across five synthetic datasets and the World Cup 2014 dataset.Comment: Will be published in Intelligent Vehicle Symposium 202

Background estimation and adaptation model with light-change removal for heavily cown-sampled video surveillance signals

This paper describes a background-subtraction system with light change-detection which works on a luminance QCIF-size video signal for surveillance applications. The new proposed pixel background model is controlled by a statistical threshold and is robust for cluttered background and small object motions. Moreover, (or light-change detection, we introduce temporal prediction of pixel values to estimate trends while quickly adapting to scene changes to facilitate a very sensitive detection of moving targets. Experiments show that a local contrast enhancement applied prior to down-sampling improves detection sensitivity, arid combined with the shifted sealed difference and me Wronskian determinant operators provides the best background/foreground detectio