All for one or one for all? Combining heterogeneous features for activity spotting

Abstract

Abstract-Choosing the right feature for motion based activity spotting is not a trivial task. Often, features derived by intuition or that proved to work well in previous work are used. While feature selection algorithms allow automatic decision, definition of features remains a manual task. We conduct a comparative study of features with very different origin. To this end, we propose a new type of features based on polynomial approximation of signals. The new feature type is compared to features used routinely for motion based activity recognition as well as to recently proposed body-model based features. Experiments were performed on three different, large datasets allowing a thorough, in-depth analysis. They not only show the respective strengths of the different feature types but also their complementarity resulting in improved performance through combination. It shows that each feature type with its individual and complementary strengths and weaknesses can improve results by combination.

Forecasting occurrences of activities

“…On the other hand, discriminative approaches that model the boundary between different activity classes offer an effective alternative. These techniques include decision trees, meta classifiers based on boosting and bagging, support vector machines, and discriminative probabilistic graphical models such as conditional random fields [33,41–43]. Other approaches combine these underlying learning algorithms, including boosting and other ensemble methods [35,44,45].…”

Collegial activity learning between heterogeneous sensors

“…These include Bayesian approaches [7], [26], [27], hidden Markov models [28]–[31], conditional random fields [10], [27], [32], support vector machines [14], decision trees [26], and ensemble methods [27], [33], [34]. Each of these approaches offers advantages in terms of amount of training that is required, model robustness, and computational cost.…”

Activity-Aware Energy-Efficient Automation of Smart Buildings

“…On the other hand, discriminative approaches that model the boundary between different activity classes offer an effective alternative. These techniques include decision trees, meta classifiers based on boosting and bagging, support vector machines, and discriminative probabilistic graphical models such as conditional random fields [20,[52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67]. Other approaches combine these underlying learning algorithms, including boosting and other ensemble methods [68][69][70][71].…”