Patch-Based Experiments with Object Classification in Video Surveillance

We present a patch-based algorithm for the purpose of object classification in video surveillance. Within detected regions-of-interest (ROIs) of moving objects in the scene, a feature vector is calculated based on template matching of a large set of image patches. Instead of matching direct image pixels, we use Gabor-filtered versions of the input image at several scales. This approach has been adopted from recent experiments in generic object-recognition tasks. We present results for a new typical video surveillance dataset containing over 9,000 object images. Furthermore, we compare our system performance with another existing smaller surveillance dataset. We have found that with 50 training samples or higher, our detection rate is on the average above 95%. Because of the inherent scalability of the algorithm, an embedded system implementation is well within reach

3D pose estimation by directly matching polyhedral models to gray value gradients

This contribution addresses the problem of pose estimation and tracking of vehicles in image sequences from traffic scenes recorded by a stationary camera. In a new algorithm, the vehicle pose is estimated by directly matching polyhedral vehicle models to image gradients without an edge segment extraction process. The new approach is significantly more robust than approaches that rely on feature extraction since the new approach exploits more information from the image data. We successfully tracked vehicles that were partially occluded by textured objects, e.g. foliage, where a previous approach based on edge segment extraction failed. Moreover, the new pose estimation approach is also used to determine the orientation and position of the road relative to the camera by matching an intersection model directly to image gradients. Results from various experiments with real world traffic scenes are presented

Motion Boundary Detection in Image Sequences by Local Stochastic Tests

Nagel H-H, Socher G, Kollnig H, Otte M. Motion Boundary Detection in Image Sequences by Local Stochastic Tests. In: Proc. 3rd European Conference on Computer Vision, ECCV’94. 1994

Why not explain? effects of explanations on human perceptions of autonomous driving

Autonomous vehicles (AVs) have the potential to change the way we commute, travel, and transport our goods. The deployment of AVs in society, however, requires that people understand, accept, and trust them. Intelligible explanations can help different AV stakeholders to assess AVs' behaviours, and in turn, increase their confidence and foster trust. In a user study (N = 101), we examined different explanation types (based on investigatory queries) provided by an AV and their effect on people using the trust determinant factors. Our quantitative and qualitative analysis shows that explanations with causal attributions improved task performance and understanding when assessing driving events but did not directly improve perceived trust. This underlines the potential need for additional measures and research to enhance trust in AVs

A fait accompli? an empirical study into the absence of consent to third-party tracking in android apps

Third-party tracking allows companies to collect users' behavioural data and track their activity across digital devices. This can put deep insights into users' private lives into the hands of strangers, and often happens without users' awareness or explicit consent. EU and UK data protection law, however, requires consent, both 1) to access and store information on users' devices and 2) to legitimate the processing of personal data as part of third-party tracking, as we analyse in this paper. This paper further investigates whether and to what extent consent is implemented in mobile apps. First, we analyse a representative sample of apps from the Google Play Store. We find that most apps engage in third-party tracking, but few obtained consent before doing so, indicating potentially widespread violations of EU and UK privacy law. Second, we examine the most common third-party tracking libraries in detail. While most acknowledge that they rely on app developers to obtain consent on their behalf, they typically fail to put in place robust measures to ensure this: disclosure of consent requirements is limited; default consent implementations are lacking; and compliance guidance is difficult to find, hard to read, and poorly maintained.Comment: This paper will be presented at the 7th Symposium on Usable Privacy and Security (SOUPS 2021), 8th-10th August 202