Select a calendar:
Filter May Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for May 02, 2014
-
PhD Defense - Prithviraj Banerjee
Fri, May 02, 2014 @ 10:30 AM - 12:30 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Candidate: Prithviraj Banerjee
Title: Incorporating Aggregate Feature Statistics in Structured Dynamical Models for Human Activity Recognition
Date: Friday, May 2nd, 2014
Time: 10:30AM
Location: PHE 223
Committee:
Ram Nevatia (Chair)
Gerard Medioni
C. -C. Jay Kuo (outside member)
Abstract:
Human action recognition in videos is a central problem of computer vision, with numerous applications in the fields of video surveillance, data mining and human computer interaction. There has been considerable research in classifying pre-segmented videos into a single activity class, however there has been comparatively less progress on activity detection in un-segmented and un-aligned videos containing medium to long term complex events. Our objective is to develop efficient algorithms to recognize human activities in monocular videos captured from static cameras in both indoor and outdoor scenarios. Our focus is on detection and classification of complex human events in un-segmented continuous videos, where the top level event is composed of primitive action components, such as human key-poses or primitive actions. We assume a weakly supervised setting, where only the top level event labels are provided for each video during training, and the primitive action components are not labeled.
We require our algorithm to be robust to missing frames, temporary occlusion of body parts, background clutter, and to variations in activity styles and durations. Furthermore, our models gracefully scale to complex events containing human-human and human-object interactions, while not assuming access to perfect pedestrian or object detection results.
We have proposed and adopted the design philosophy of combining global statistics of local spatio-temporal features, with the high level structure and constraints provided by dynamic probabilistic graphical models. We present four different algorithms for activity recognition, spanning the feature-classifier hierarchy in terms of their semantic and structure modeling capability. Firstly, we present a novel Latent CRF classifier for modeling the local neighborhood structure of spatio-temporal interest point features in terms of code-word co-occurrence statistics, which captures the local temporal dynamics present in the action. In our second work, we present a multiple kernel learning framework to combine human pose estimates generated from a collection of kinematic tree priors, spanning the range of expected pose dynamics in human actions. In our third work, we present a latent CRF model for automatically identifying and inferring the temporal location of key-poses of an activity, and show results on detecting multiple instances of actions in continuous un-segmented videos. Lastly, we propose a novel dynamic multi-state feature pooling algorithm which identifies the discriminative segments of a video, and is robust to arbitrary gaps between state transitions, and also to significant variations in state durations. We evaluate our models on short, medium and long term activity datasets, and show state of the art performance on both classification, detection and video streaming tasks.
Location: 223
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
-
PhD Defense - Pramod Sharma
Fri, May 02, 2014 @ 03:45 PM - 05:45 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Pramod Sharma
Title: Effective Incremental Learning and Detector Adaptation Methods for Video Object Detection
Date: Friday, May 2nd, 2014
Time: 3:45 PM
Location EEB 248
Committee:
Ram Nevatia (chair)
Gerard Medioni
C. -C. Jay Kuo (outside member)
Abstract:
Object detection is a challenging problem in Computer Vision. With increasing use of social media, smart phones and modern digital cameras thousands of videos are uploaded on the Internet everyday. Object detection is very critical for analyzing these videos for many tasks such as summarization, description, scene analysis, tracking or activity recognition.
Typically, an object detector is trained in an offline manner by collecting thousands of positive and negative training samples. However, due to large variations in appearance, pose, illumination, background scene and similarity to other objects; it is very difficult to train a generalized object detector that can give high performance across different test videos. We address this problem by proposing detector adaptation methods which collect online samples from a given text video and train an adaptive/incremental classifier using this training data in order to achieve high performance.
First we propose an efficient incremental learning method for cascade of boosted classifiers, which collects training data in a supervised manner and adjusts the parameters of offline trained cascade of boosted classifiers by combining online loss with offline loss. Then, we propose an unsupervised incremental learning approach which collects online samples automatically from a given test video using tracking information. However online samples collected in an unsupervised manner are prone to the labeling errors, hence instead of assigning hard labels to online samples, we utilize Multiple Instance Learning (MIL) approach and assign labels to the bags of instances not to the individual samples. We propose an MIL loss function for Real Adaboost framework to train our incremental detector.
While the above approach gives good performance, it is limited to Real Adaboost based offline trained detector. We propose an efficient detector adaptation method which works with various kinds of offline trained detectors. In this approach first we apply offline trained detector at a high threshold to obtain confident detection responses. These detection responses are tracked using a tracking-by-detection method and using obtained detection responses and tracking output online samples are collected. However, positive online samples can have different articulations and pose variations. Hence they are divided into different categories using a pose classifier trained in the offline setting. We train a multi-class random fern adaptive classifier using collected online samples. During testing stage, first we apply offline trained detector at a low threshold, then we apply adaptive classifier on the obtained detection responses, which either accepts the detection response as a true response or rejects it as the false alarm. In this manner, we focus on improving the precision of offline trained detector.
We extend this approach by proposing a multi-class boosted random fern adaptive classifier in order to select discriminative random ferns for high detection performance. We further incorporate MIL in boosted random fern framework and propose a boosted multi instance random fern adaptive classifier. Boosting provides discriminability to the adaptive classifier, whereas MIL provides robustness towards noisy and ambiguous training samples. We demonstrate the effectiveness of our approaches by evaluating them on several public datasets for the problem of human detection.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
