QuickSpot File Compare 4.2 serial key or number

Vehicles searching for a vacant parking spot on the street can amount to as much as 40 % of the traffic in certain city areas, thus largely affecting mobility in urban environments. For this reason, it would be desirable to create integrated smart traffic management systems capable of providing real-time information to drivers about the location of available vacant parking spots. A scalable solution would consist in exploiting the existing and widely-deployed video surveillance camera networks, which requires the development of computer vision algorithms for detecting vacant parking spots. Following this idea, this work introduces QuickSpot, a car-driven video analytics solution for on-street vacant parking spot detection designed as a motion detection, object tracking and visual recognition pipeline. One of the main features of QuickSpot is its simplified setup, as it can be trained on external databases to learn the appearances of the objects it is capable of recognizing (pedestrians and vehicles). To test its performance under different daytime lighting conditions, we have recorded, edited, annotated and made available to the research community the QuickSpotDB video database for the vacant parking spot detection problem. In the conducted experiments, we have evaluated the trade-off between the accuracy and the computational complexity of QuickSpot with an eye to its practical applicability. The results show that QuickSpot detects parking spot status with an average accuracy close to 99 % at a 1-second rate regardless of the illumination conditions, outperforming in an indirect comparison the other car-driven approaches reported in the literature.

1. 1.

   The QuickSpotDB video database is available upon request to the corresponding author.

2. 2.

   As for the configuration of the classifier, initial validation experiments (not reported here for the sake of brevity) allowed to observe that k = 1 nearest neighbor yielded the highest recognition accuracy. Moreover, the Euclidean distance is used to measure the similarity between patterns.

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   Available online at http://cbcl.mit.edu/software-datasets/PedestrianData.html (last accessed in May 2016).

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   Available online at http://viper-toolkit.sourceforge.net/ (last accessed in May 2016)

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Affiliations

1. GTM - Grup de Recerca en Tecnologies Mèdia, La Salle - Universitat Ramon Llull, Quatre Camins 30, 08022, Barcelona, Spain

   Elena Màrmol & Xavier Sevillano

Corresponding author

Correspondence to Xavier Sevillano.

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