A service robot that operates in a previously-unseen home environment should be able to recognize the functionality of the rooms it visits, such as a living room, a bathroom, etc. We present a novel part-based model and an approach for room categorization using data obtained from a visual sensor. Images are represented with sets of unordered parts that are obtained by object-agnostic region proposals, and encoded using state-of-the-art image descriptor extractor - a convolutional neural network (CNN). An approach is proposed that learns category-specific discriminative parts for the part-based model. The proposed approach was compared to the state-of-the-art CNN trained specifically for place recognition. Experimental results show that the proposed approach outperforms the holistic CNN by being robust to image degradation, such as occlusions, modifications of image scaling, and aspect changes. In addition, we report non-negligible annotation errors and image duplicates in a popular dataset for place categorization and discuss annotation ambiguities.

We present a novel dataset for evaluation of object matching and recognition methods in surveillance scenarios. Dataset consists of more than 23,000 images, depicting 15 persons and nine vehicles. A ground truth data - the identity of each person or vehicle - is provided, along with the coordinates of the bounding box in the full camera image. The dataset was acquired from 36 stationary camera views using a variety of surveillance cameras with resolutions ranging from standard VGA to three megapixel. 27 cameras observed the persons and vehicles in an outdoor environment, while the remaining nine observed the same persons indoors. The activity of persons was planned in advance, they drive the cars to the parking lot, exit the cars and walk around the building, through the main entrance, and up the stairs, towards the first floor of the building. The intended use of the dataset is performance evaluation of computer vision methods that aim to (re)identify people and objects from many different viewpoints in different environments and under variable conditions. Due to variety of camera locations, vantage points and resolutions, the dataset provides means to adjust the difficulty of the identification task in a controlled and documented manner. An interface for easy use of dataset within Matlab is provided as well, and the data is complemented by baseline results using a basic color histogram-based descriptor. While the cropped images of persons and vehicles represent the primary data in our dataset, we also provide full-frame images and a set of tracklets for each object as a courtesy to the dataset users.