6,160 research outputs found
Object Tracking with Multiple Instance Learning and Gaussian Mixture Model
Recently, Multiple Instance Learning (MIL) technique has been introduced for object tracking\linebreak applications, which has shown its good performance to handle drifting problem. While some instances in positive bags not only contain objects, but also contain the background, it is not reliable to simply assume that each feature of instances in positive bags obeys a single Gaussian distribution. In this paper, a tracker based on online multiple instance boosting has been developed, which employs Gaussian Mixture Model (GMM) and single Gaussian distribution respectively to model features of instances in positive and negative bags. The differences between samples and the model are integrated into the process of updating the parameters for GMM. With the Haar-like features extracted from the bags, a set of weak classifiers are trained to construct a strong classifier, which is used to track the object location at a new frame. And the classifier can be updated online frame by frame. Experimental results have shown that our tracker is more stable and efficient when dealing with the illumination, rotation, pose and appearance changes
Neural-Augmented Static Analysis of Android Communication
We address the problem of discovering communication links between
applications in the popular Android mobile operating system, an important
problem for security and privacy in Android. Any scalable static analysis in
this complex setting is bound to produce an excessive amount of
false-positives, rendering it impractical. To improve precision, we propose to
augment static analysis with a trained neural-network model that estimates the
probability that a communication link truly exists. We describe a
neural-network architecture that encodes abstractions of communicating objects
in two applications and estimates the probability with which a link indeed
exists. At the heart of our architecture are type-directed encoders (TDE), a
general framework for elegantly constructing encoders of a compound data type
by recursively composing encoders for its constituent types. We evaluate our
approach on a large corpus of Android applications, and demonstrate that it
achieves very high accuracy. Further, we conduct thorough interpretability
studies to understand the internals of the learned neural networks.Comment: Appears in Proceedings of the 2018 ACM Joint European Software
Engineering Conference and Symposium on the Foundations of Software
Engineering (ESEC/FSE
- …