In this paper, we present a novel onboard robust visual algorithm for long-term arbitrary 2D and 3D object tracking using a reliable global-local object model for unmanned aerial vehicle (UAV) applications, e.g., autonomous tracking and chasing a moving target. The first main approach in this novel algorithm is the use of a global matching and local tracking approach. In other words, the algori...

This paper studies a new data mining problem called multiinstance outlier identification. This problem arises in tasks where each sample consists of many alternative feature vectors (instances) that describe it. This paper defines the multi-instance outliers and analyzes the basic types of multiinstance outliers. Two general identification approaches are proposed based on the state-of-the-art (...

Outlier detection aims at searching for a small set of objects that are inconsistent or considerably deviating from other objects in a dataset. Existing research focuses on outlier identification while omitting the equally important problem of outlier interpretation. This paper presents a novel method named LODI to address both problems at the same time. In LODI, we develop an approach that exp...

Deep Neural Networks (DNNs) are extensively deployed in today’s safety-critical autonomous systems thanks to their excellent performance. However, they known make mistakes unpredictably, e.g., a DNN may misclassify an object if it is used for perception, or issue unsafe control commands planning and control. One common cause such unpredictable Out-of-Distribution (OOD) input samples, i.e., samp...

Outlier detection concerns discovering some unusual data whose behavior is exceptional compared to other data. In contrast to non-spatial outliers which only consider non-spatial attributes, spatial outliers are defined to be those sites which are very different from its neighbors defined in terms of spatial attributes, i.e., locations. In this paper, we propose a local trimmed mean approach to...

Outlier detection is very interesting, useful and challenging problem in the field of data mining. Because of sparse data clustering algorithm which are based on distance will not work to find outliers in spatial data. Problem of finding irregular feature in spatial data need to be explore. Many existing approaches have been proposed to overcome the problem of outlier detection in spatial Geogr...

An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism (Hawkins, 1980). Outlier detection has many applications, such as data cleaning, Fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that have behavior very different to the most of the individuals of the...

An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism (Hawkins, 1980). Outlier detection has many applications, such as data cleaning, fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that have behavior very different from the most of the individuals of t...