Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos

We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hundreds of thousands of object instances. We propose criteria for reliable object detection and tracking for constraining the semi-supervised learning process and minimizing semantic drift. Our approach does not assume exhaustive labeling of each object instance in any single frame, or any explicit annotation of negative data. Working in such a generic setting allow us to tackle multiple object instances in video, many of which are static. In contrast, existing approaches either do not consider multiple object instances per video, or rely heavily on the motion of the objects present. The experiments demonstrate the effectiveness of our approach by evaluating the automatically labeled data on a variety of metrics like quality, coverage (recall), diversity, and relevance to training an object detector.Comment: To appear in CVPR 201

Foreign object detection (FOD) using multi-class classifier with single camera vs. distance map with stereo configuration

Detection of objects of interest is a fundamental problem in computer vision. Foreign object detection (FOD) is to detect the objects that are not expected to be appear in certain area. For this task, we need to first detect the position of foreign objects, and then compute the distance to the foreign objects to judge whether the objects are within the dangerous zone or not. The three principle sources of difficulty in performing this task are: a) the huge number of foreign objects categories, b) the calculation of distance using camera(s), and c) the real-time system running performance. Most state-of-art detectors focus on one type or one class of objects. To the best of our knowledge, there is no single solution that focuses on a set of multiple foreign objects detection in an integrated manner. In some cases, multiple detectors can operate simultaneously to detect objects of interest in a given input. This is not efficient. The goal of our research is to focus on detection of a set of objects identified as foreign object in an integrated and efficient manner. We design a multi-class detector. Our approach is to use a coarse-tofine strategy in which we divide the complicated space into finer and finer sub-spaces. For this purpose, data-driven clustering algorithm is implemented to gather similar foreign objects samples, and then an extended vector boosting algorithm is developed to train our multi-class classifier. The purpose of the extended vector boosting algorithm is to separate all foreign objects from background. For the task of estimation of the distance to the foreign objects, we design a look-up table which is based on the area of the detected foreign objects. Furthermore, we design a FOD framework. Our approach is to use stereo matching algorithm to get the disparity information based on intensity images from stereo cameras, and then using the camera model to retrieve the distance information. The distance calculated using disparity is more accurate than using the distance look-up table. We calculate the initial distance map when no objects are in the scene. Block of interest (BOI) is the area where distance is smaller than the corresponding area in the initial distance map. For the purpose of detecting foreign objects, we use flood fill method along with noise suppression method to combine adjacent BOI with higher confidence level.The foreign object detection prototype system has been implemented and evaluated on a number of test sets under real working scenarios. The experimental results show that our algorithm and framework are efficient and robust

Detecting Outliers in Data with Correlated Measures

Advances in sensor technology have enabled the collection of large-scale datasets. Such datasets can be extremely noisy and often contain a significant amount of outliers that result from sensor malfunction or human operation faults. In order to utilize such data for real-world applications, it is critical to detect outliers so that models built from these datasets will not be skewed by outliers. In this paper, we propose a new outlier detection method that utilizes the correlations in the data (e.g., taxi trip distance vs. trip time). Different from existing outlier detection methods, we build a robust regression model that explicitly models the outliers and detects outliers simultaneously with the model fitting. We validate our approach on real-world datasets against methods specifically designed for each dataset as well as the state of the art outlier detectors. Our outlier detection method achieves better performances, demonstrating the robustness and generality of our method. Last, we report interesting case studies on some outliers that result from atypical events.Comment: 10 page

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure