Making Action Recognition Robust to Occlusions and Viewpoint Changes

Most state-of-the-art approaches to action recognition rely on global representations either by concatenating local information in a long descriptor vector or by computing a single location independent histogram. This limits their performance in presence of occlusions and when running on multiple viewpoints. We propose a novel approach to providing robustness to both occlusions and viewpoint changes that yields significant improvements over existing techniques. At its heart is a local partitioning and hierarchical classification of the 3D Histogram of Oriented Gradients (HOG) descriptor to represent sequences of images that have been concatenated into a data volume. We achieve robustness to occlusions and viewpoint changes by combining training data from all viewpoints to train classifiers that estimate action labels independently over sets of HOG blocks. A top level classifier combines these local labels into a global action class decision.

Robust Activity Recognition Combining Anomaly Detection and Classifier Retraining

Ricardo Andres Chavarriaga Lozano, Daniel Roggen, Hesam Sagha

Activity recognition systems based on body-worn motion sensors suffer from a decrease in performance during the deployment and run-time phases, because of probable changes in the sensors (e.g. displacement or rotation), which is the case in many real-life scenarios (e.g. mobile phone in a pocket). Existing approaches to achieve robustness tend to sacrifice information (e.g. by rotation-invariant features) or reduce the weight of the anomalous sensors at the classifier fusion stage (adaptive fusion), ignoring data which might still be perfectly meaningful, although different from the training data. We propose to use adaptation to rebuild the classifier models of the sensors which have changed position by a two-step approach: in the first step, we run an anomaly detection algorithm to automatically detect which sensors are delivering unexpected data; subsequently, we trigger a system self-training process, so that the remaining classifiers retrain the “anomalous” sensors. We show the benefit of this approach in a real activity recognition dataset comprising data from 8 sensors to recognize locomotion. The approach achieves similar accuracy compared to the upper baseline, obtained by retraining the anomalous classifiers on the new data.

2013

Robust Activity Recognition Combining Anomaly Detection and Classifier Retraining

Ricardo Andres Chavarriaga Lozano, Daniel Roggen, Hesam Sagha

Activity recognition systems based on body-worn motion sensors suffer from a decrease in performance during the deployment and run-time phases, because of probable changes in the sensors (e.g. displacement or rotatation), which is the case in many real-life scenarios (e.g. mobile phone in a pocket). Existing approaches to achieve robustness tend to sacrifice information (e.g. by rotation-invariant features) or reduce the weight of the anomalous sensors at the classifier fusion stage (adaptive fusion), ignoring data which might still be perfectly meaningful, although different from the training data. We propose to use adaptation to rebuild the classifier models of the sensors which have changed position by a two-step approach: in the first step, we run an anomaly detection algorithm to automatically detect which sensors are delivering unexpected data; subsequently, we trigger a system self-training process, so that the remaining classifiers retrain the "anomalous" sensors. We show the benefit of this approach in a real activity recognition dataset comprising data from 8 sensors to recognize locomotion. The approach achieves similar accuracy compared to the upper baseline, obtained by retraining the anomalous classifiers on the new data.

IEEE2013

On-line Independent Support Vector Machines for Cognitive Systems

Barbara Caputo, Jie Luo

Learning from experience and adapting to changing stimuli are fundamental capabilities for artificial cognitive systems. This calls for on-line learning methods able to achieve high accuracy while at the same time using limited computer power. Research on autonomous agents has been actively investigating these issues, mostly using probabilistic frameworks and within the context of navigation and learning by imitation. Still, recent results on robot localization have clearly pointed out the potential of discriminative classifiers for cognitive systems. In this paper we follow this approach and propose an on-line version of the Support Vector Machine (SVM) algorithm. Our method, that we call On-line Independent SVM, builds a solution on-line, achieving an excellent accuracy vs.~compactness trade-off. In particular the size of the obtained solution is always bounded, implying a bounded testing time. At the same time, the algorithm converges to the optimal solution at each incremental step, as opposed to similar approaches where optimality is achieved in the limit of infinite number of training data. These statements are supported by experiments on standard benchmark databases as well as on two real-world applications, namely

(a)

place recognition by a mobile robot in an indoor environment, and

(b)

human grasping posture classification.

IDIAP2007

On-line Independent Support Vector Machines for Cognitive Systems

Barbara Caputo, Jie Luo

(a)

place recognition by a mobile robot in an indoor environment, and

(b)

human grasping posture classification.

IDIAP2007