Spatiotemporal Stacked Sequential Learning for Pedestrian Detection

Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera.Comment: 8 pages, 5 figure, 1 tabl

Vehicles Recognition Using Fuzzy Descriptors of Image Segments

In this paper a vision-based vehicles recognition method is presented. Proposed method uses fuzzy description of image segments for automatic recognition of vehicles recorded in image data. The description takes into account selected geometrical properties and shape coefficients determined for segments of reference image (vehicle model). The proposed method was implemented using reasoning system with fuzzy rules. A vehicles recognition algorithm was developed based on the fuzzy rules describing shape and arrangement of the image segments that correspond to visible parts of a vehicle. An extension of the algorithm with set of fuzzy rules defined for different reference images (and various vehicle shapes) enables vehicles classification in traffic scenes. The devised method is suitable for application in video sensors for road traffic control and surveillance systems.Comment: The final publication is available at http://www.springerlink.co