Detection Of Anomalous Objects Research Articles

Detection of Anomalous Objects on the Soil by Estimating Depth Map from RGB Image

Detection of Anomalous Objects on the Soil by Estimating Depth Map from RGB Image

The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation

Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect failure is key for safety-critical applications like autonomous driving. Existing uncertainty estimates have mostly been evaluated on simple tasks, and it is unclear whether these methods generalize to more complex scenarios. We present Fishyscapes, the first public benchmark for anomaly detection in a real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects. We adapt state-of-the-art methods to recent semantic segmentation models and compare uncertainty estimation approaches based on softmax confidence, Bayesian learning, density estimation, image resynthesis, as well as supervised anomaly detection methods. Our results show that anomaly detection is far from solved even for ordinary situations, while our benchmark allows measuring advancements beyond the state-of-the-art. Results, data and submission information can be found at https://fishyscapes.com/.

Anomalous object detection by active search with PTZ cameras

Due to the large amount of visual information generated daily, proposals that automatically analyze and process data are becoming increasingly necessary. This work focuses on the detection of anomalous objects in video sequences captured by PTZ (pan-tilt-zoom) cameras, considering as anomalies the objects which belong to categories that should not appear in a specific scene (e.g. pedestrians on a highway). There is a lack in the previous literature of a principled approach for the control of PTZ cameras that takes advantage of the recent developments in deep learning-based object detection. Our proposal aims to fill this gap by offering a probabilistic framework where the guidance of PTZ cameras is accommodated. The proposed methodology involves three different modules. An object detection stage, where deep learning networks are used to detect the objects that appear in the scene; an anomalous detection module, where a mixture of Dirichlet distributions is considered to detect automatically, and without supervised training, those detected objects which are likely to be anomalous; and finally, a PTZ camera controller which allows to follow and focus on the object considered as the most probably anomalous in the scene. The experimental results show the performance and viability of our proposal, which outperforms several competitors from qualitative and quantitative points of view.

Efficient anomaly detection from medical signals and images

Anomaly detection is a very vital area in medical signal and image processing due to its importance in automatic diagnosis. This paper presents three efficient anomaly detection approaches for applications related to Electroencephalogram (EEG) signal processing and retinal image processing. The first approach depends on the utilization of Scale-Invariant Feature Transform (SIFT) for automatic seizure detection. The second one is based on the utilization of digital filters in a statistical framework for seizure prediction. Finally, an automated Diabetic Retinopathy (DR) diagnosis approach is presented based on the segmentation and detection of anomalous objects from retinal images. The presented simulation results reveal the success of the proposed techniques towards automated medical diagnosis.