Particle Filter for Targets Tracking with Motion Model

Real-time robust tracking for multiple non-rigid objects is a challenging task in computer vision research. In recent years, stochastic sampling based particle filter has been widely used to describe the complicated target features of image sequence. In this paper, non-parametric density estimation and particle filter techniques are employed to model the background and track the object. Color feature and motion model of the target are extracted and used as key features in the tracking step, in order to adapt to multiple variations in the scene, such as background clutters, object's scale change and partial overlap of different targets. The paper also presents the experimental result on the robustness and effectiveness of the proposed method in a number of outdoor and indoor visual surveillance scenes.published_or_final_versio

Realistic simulation of spatial computers and robot swarms

The goal of Amorphous Computing is defined as: “To identify organizational principles and create programming technologies for obtaining intentional, pre-specified behavior from the cooperation of myriad unreliable parts that are arranged in unknown, irregular, and time-varying ways” [1]. Amorphous Facades are stationary formations of amorphous computers used in building environments and are constructed as a wall. One of the desired functionalities of the Amorphous walls is to be able to track occupancy within an interior environment. Pymorphous is a spatial computing library for Python. Currently, Pymorphous has its own simulator, but the simulator is very abstract and doesn\u27t realistically simulate physical robots or device hardware limitations. Webots is a virtual robot simulation program that is much less abstract that the Pymorphous simulator and that accurately simulates physics and realistic hardware. The simulator-runtime for Pymorphous is very specific to its own simulator. To allow Pymorphous to be simulated in a less abstract environment, Webots, I will create a new runtime which will facilitate communication between amorphous computing robots within Webots and Pymorphous. To demonstrate the functionality of the Webots-runtime for Pymorphous, I will develop three simulations within Webots. A simple neighborhood simulation will be used to show the functionality of Pymorphous neighborhood calculation between amorphous wall panels in Webots. A velocity tracking simulation will be used to demonstrate the functionality of simple tracking algorithms within Webots, similar to algorithms that the wall might actually use to track occupancy. Lastly, the setup of the Amorphous Wall within Webots will be changed to reflect mobile robots to illustrate the ability of Webots to simulate more complex Pymorphous flocking algorithms on mobile robots

Identity Retention of Multiple Objects under Extreme Occlusion Scenarios using Feature Descriptors

Identity assignment and retention needs multiple object detection and tracking. It plays a vital role in behavior analysis and gait recognition. The objective of Multiple Object Tracking (MOT) is to detect, track and retain identities from an image sequence. An occlusion is a major resistance in identity retention. It is a challenging task to handle occlusion while tracking varying number of person in the complex scene using a monocular camera. In MOT, occlusion remains a challenging task in real world applications. This paper uses Gaussian Mixture Model (GMM) and Hungarian Assignment (HA) for person detection and tracking. We propose an identity retention algorithm using Rotation Scale and Translation (RST) invariant feature descriptors. In addition, a segmentation based optimum demerge handling algorithm is proposed to retain proper identities under occlusion. The proposed approach is evaluated on a standard surveillance dataset sequences and it achieves 97 % object detection accuracy and 85% tracking accuracy for PETS-S2.L1 sequence and 69.7% accuracy as well as 72.3% precision for Town Centre Sequence

Multi-class object tracking algorithm that handles fragmentation and grouping

We propose a framework for detecting and tracking multiple interacting objects, while explicitly handling the dual problems of fragmentation (an object may be broken into several blobs) and grouping (multiple objects may appear as a single blob). We use foreground blobs obtained by background subtraction from a stationary camera as measurements. The main challenge is to associate blob measurements with objects, given the fragment-object-group ambiguity when the number of objects is variable and unknown, and object-class-specific models are not available. We first track foreground blobs till they merge or split. We then build an inference graph representing merge-split relations between the tracked blobs. Using this graph and a generic object model based on spatial connectedness and coherent motion, we label the tracked blobs as whole objects, fragments of objects or groups of interacting objects. The outputs of our algorithm are entire tracks of objects, which may include corresponding tracks from groups during interactions. Experimental results on multiple video sequences are shown. 1

Improved robustness and efficiency for automatic visual site monitoring

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 219-228).Knowing who people are, where they are, what they are doing, and how they interact with other people and things is valuable from commercial, security, and space utilization perspectives. Video sensors backed by computer vision algorithms are a natural way to gather this data. Unfortunately, key technical issues persist in extracting features and models that are simultaneously efficient to compute and robust to issues such as adverse lighting conditions, distracting background motions, appearance changes over time, and occlusions. In this thesis, we present a set of techniques and model enhancements to better handle these problems, focusing on contributions in four areas. First, we improve background subtraction so it can better handle temporally irregular dynamic textures. This allows us to achieve a 5.5% drop in false positive rate on the Wallflower waving trees video. Secondly, we adapt the Dalal and Triggs Histogram of Oriented Gradients pedestrian detector to work on large-scale scenes with dense crowds and harsh lighting conditions: challenges which prevent us from easily using a background subtraction solution. These scenes contain hundreds of simultaneously visible people. To make using the algorithm computationally feasible, we have produced a novel implementation that runs on commodity graphics hardware and is up to 76 faster than our CPU-only implementation. We demonstrate the utility of this detector by modeling scene-level activities with a Hierarchical Dirichlet Process.(cont.) Third, we show how one can improve the quality of pedestrian silhouettes for recognizing individual people. We combine general appearance information from a large population of pedestrians with semi-periodic shape information from individual silhouette sequences. Finally, we show how one can combine a variety of detection and tracking techniques to robustly handle a variety of event detection scenarios such as theft and left-luggage detection. We present the only complete set of results on a standardized collection of very challenging videos.by Gerald Edwin Dalley.Ph.D

Physical Reasoning for Intelligent Agent in Simulated Environments

Developing Artificial Intelligence (AI) that is capable of understanding and interacting with the real world in a sophisticated way has long been a grand vision of AI. There is an increasing number of AI agents coming into our daily lives and assisting us with various daily tasks ranging from house cleaning to serving food in restaurants. While different tasks have different goals, the domains of the tasks all obey the physical rules (classic Newtonian physics) of the real world. To successfully interact with the physical world, an agent needs to be able to understand its surrounding environment, to predict the consequences of its actions and to draw plans that can achieve a goal without causing any unintended outcomes. Much of AI research over the past decades has been dedicated to specific sub-problems such as machine learning and computer vision, etc. Simply plugging in techniques from these subfields is far from creating a comprehensive AI agent that can work well in a physical environment. Instead, it requires an integration of methods from different AI areas that considers specific conditions and requirements of the physical environment. In this thesis, we identified several capabilities that are essential for AI to interact with the physical world, namely, visual perception, object detection, object tracking, action selection, and structure planning. As the real world is a highly complex environment, we started with developing these capabilities in virtual environments with realistic physics simulations. The central part of our methods is the combination of qualitative reasoning and standard techniques from different AI areas. For the visual perception capability, we developed a method that can infer spatial properties of rectangular objects from their minimum bounding rectangles. For the object detection capability, we developed a method that can detect unknown objects in a structure by reasoning about the stability of the structure. For the object tracking capability, we developed a method that can match perceptually indistinguishable objects in visual observations made before and after a physical impact. This method can identify spatial changes of objects in the physical event, and the result of matching can be used for learning the consequence of the impact. For the action selection capability, we developed a method that solves a hole-in-one problem that requires selecting an action out of an infinite number of actions with unknown consequences. For the structure planning capability, we developed a method that can arrange objects to form a stable and robust structure by reasoning about structural stability and robustness