4 ottob 2013 anni - 17. Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers

Descrizione:

This paper
presents a new computer vision based algorithm for recognizing single actions of earthmoving construction
equipment. This is particularly a challenging task as equipment can be partially occluded in site
video streams and usually come in wide variety of sizes and appearances. The scale and pose of the equipment
actions can also significantly vary based on the camera configurations. In the proposed method, a
video is initially represented as a collection of spatio-temporal visual features by extracting space–time
interest points and describing each feature with a Histogram of Oriented Gradients (HOG). The algorithm
automatically learns the distributions of the spatio-temporal features and action categories using amulticlass
Support Vector Machine (SVM) classifier. This strategy handles noisy feature points arisen from typical
dynamic backgrounds. Given a video sequence captured from a fixed camera, the multi-class SVM
classifier recognizes and localizes equipment actions. For the purpose of evaluation, a new video dataset
is introduced which contains 859 sequences from excavator and truck actions. This dataset contains large
variations of equipment pose and scale, and has varied backgrounds and levels of occlusion. The experimental
results with average accuracies of 86.33% and 98.33% show that our supervised method outperforms
previous algorithms for excavator and truck action recognition. The results hold the promise for
applicability of the proposed method for construction activity analysis.

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