e-book Multi-Camera Networks: Principles and Applications

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The approach, although not limited to this application, is applied for learning a person detector on different challenging scenarios. In fact, we can show that even though starting from a very small number of labeled samples we finally obtain a classifier yielding state-of-the-art detection results for which typically 10,s of labeled samples are required! To demonstrate the proposed approach in the following we show two experiments: I scene specific on-line co-training and evaluation on the lab scenario.

II generalizing classifier: on-line training on the lab scenario; evaluation on independent test sets. To play the videos, just click the corresponding images! For Experiment I the left and the right video show the detection results obtained by evaluating the initial and the finally obtained classifier, respectively. The video in the middle is a visualization of the updates performed during co-training: a red bounding box indicates a negative update, a green bounding box an identified positive update, and a white bounding-box a detection that is not used for updating.

Please note, that for initializing the training process only 5! For Experiment II we want to show that a classifier that was trained on our lab scenario also generalizes to different scenarios. For that purpose initialized a classifier as described above, co-trained it from multiple cameras in our lab, and applied the finally obtained classifier to a totally different data set.


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The detection results for the initial and final classifier, respectively, are shown below. First, we demonstrate the on-line learning behavior of the proposed approaches. For that purpose, we trained an initial classifier using a small number of labeled samples. The classifier was cloned and used to initialize the co-training process for each camera. Later these initial classifiers were updated by co-training. To demonstrate the learning progress, after a pre-defined number of processed training frames we saved the corresponding classifier, which was then evaluated on an independent test sequence.

Next, we give a competitive study compared to state-of-the-art person detectors for the Lab Scenario as well as for the Forecourt Scenario. In addition to the adaptive methods described above, we compared the results to fixed persons detectors, i.

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