Events Calendar

PhD Defense - Pramod Sharma

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