# Introduction¶

In this section we briefly describe the proposed approach.

Image data: we use the Kinect device. Kinect sensor is a horizontal bar connected to a small base with a motorized pivot. The major device features are RGB camera and depth sensor. The device has a USB2 interface and the resolution of the RGB camera is $$640 \times 480$$ with 8 bit quantization. The depth camera instead has a resolution of $$640 \times 480$$ with 11 bit quantization.

Pipeline: our detection pipeline analyzes the RGB (intensity) and depth video streams independently. This means that the RGB left object proposals are found without considering the depth data and the depth proposals are found without considering the RGB data. Both sets of proposal are combined later in a processing stage. The independent processing warranted because the RGB video stream is defined everywhere, i.e. for each pixel of a stream frame the intensity value is defined, but it is liable to photometric variations. Instead the depth video stream is not defined everywhere. The depth value is only available for the image regions that are close enough to the device. Also for black objects the sensor can’t measure the depth value.

By using the two video streams a background models for depth and RGB are computed. To extract left luggage proposals the spatial changes over time are accumulated in an image aggregator. For the depth aggregator we provide more than one method to accumulate the depth changes. If the aggregator exceeds a threshold is segmented with a bounding box and we mark the spatial region as left item proposal. The depth and intensity proposal are compared using the PASCAL criterion. The bounding boxes that satisfy the criterion are considered left objects.