Simple Online and Realtime Tracking with a Deep Association Metric

Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms. In this paper, we integrate appearance information to improve the performance of SORT. Due to this extension we are able to track objects through longer periods of occlusions, effectively reducing the number of identity switches. In spirit of the original framework we place much of the computational complexity into an offline pre-training stage where we learn a deep association metric on a large-scale person re-identification dataset. During online application, we establish measurement-to-track associations using nearest neighbor queries in visual appearance space. Experimental evaluation shows that our extensions reduce the number of identity switches by 45%, achieving overall competitive performance at high frame rates.Comment: 5 pages, 1 figur

Recurrent Neural Networks and Matrix Methods for Cognitive Radio Spectrum Prediction and Security

In this work, machine learning tools, including recurrent neural networks (RNNs), matrix completion, and non-negative matrix factorization (NMF), are used for cognitive radio problems. Specifically addressed are a missing data problem and a blind signal separation problem. A specialized RNN called Cellular Simultaneous Recurrent Network (CSRN), typically used in image processing applications, has been modified. The CRSN performs well for spatial spectrum prediction of radio signals with missing data. An algorithm called soft-impute for matrix completion used together with an RNN performs well for missing data problems in the radio spectrum time-frequency domain. Estimating missing spectrum data can improve cognitive radio efficiency. An NMF method called tuning pruning is used for blind source separation of radio signals in simulation. An NMF optimization technique using a geometric constraint is proposed to limit the solution space of blind signal separation. Both NMF methods are promising in addressing a security problem known as spectrum sensing data falsification attack

Modeling and tracking relative movement of object parts

Video surveillance systems play an important role in many civilian and military applications, for the purposes of security and surveillance. Object detection is an important component in a video surveillance system, used to identify possible objects of interest and to generate data for tracking and analysis purposes. Not much exploration has been done to track the moving parts of the object which is being tracked. Some of the promising techniques like Kalman Filter, Mean-shift algorithm, Matching Eigen Space, Discrete Wavelet Transform, Curvelet Transform, Distance Metric Learning have shown good performance for keeping track of moving object. Most of this work is focused on studying and analyzing various object tracking techniques which are available. Most of the techniques which are available for object tracking have heavy computation requirements. The intention of this research is to design a technique, which is not computationally intensive and to be able to track relative movements of object parts in real time. The research applies a technique called foreground detection (also known as background subtraction) for tracking the object as it is not computationally intensive. For tracking the relative movement of object parts, a skeletonization technique is used. During implementation, it is found that using skeletonization technique, it is harder to extract the objects parts