Automatic Available Seat Counting In Public Rail Transport Using Wavelets Pieterjan De Potter 1 , Ioannis Kypraios 2 , Steven Verstockt 1,3 , Chris Poppe 1 , Rik Van de Walle 1 1 Department of Electronics and Information Systems, Multimedia Lab, Ghent University – IBBT Gaston Crommenlaan 8, bus 201, B-9050 Ledeberg-Ghent, Belgium 2 School of Engineering and Design, University of Sussex Falmer Brighton, BN1 9QT, United Kingdom 3 ELIT Lab, University College West Flanders, Ghent University Association Graaf Karel de Goedelaan 5, 8500 Kortrijk, Belgium E-mail: Pieterjan.DePotter@UGent.be Abstract—Previously, we introduced an available seat counting algorithm in public rail transport. The main disadvantage of that algorithm is that it lacks automatic event detection. In this paper, we implement two automatic wavelet-based available seat count- ing algorithms. The new algorithms employ the spatial-domain Laplacian-of-Gaussian-based wavelet, and the frequency-domain Non-Linear Difference of Gaussians-based wavelet bandpass video scene filter to extract illumination invariant scene features and to combine them efficiently into the background reference frame. Manual segmentation of the scene into rectangles and tiles to detect seated objects is no longer needed as we now apply a boundary box tracker on the segmented moving objects’ blobs. We test all the algorithms with different video sequences in passengers’ train coaches, and compare the previous approach with the two new automatic wavelet-based available seat counting algorithms, and an additional spatial-domain automatic non- wavelet based Simple Mixture of Gaussian Models. Keywords—video analysis, seat counting, public transport, wavelets I. I NTRODUCTION Over the past decade, the number of installed video surveil- lance cameras has grown exponentially because of the reduced cost and the fact that security has gained importance over privacy in some scenarios. This has led to the development of different video analytics systems to detect different sce- narios’ events [1], [2], [3]. In public transport as well, video surveillance cameras are being installed, and video analytics are becoming helpful. However, the different conditions in vehicles turn the video analytics’ task difficult. The primary goal is to provide additional security, but as the cameras are already installed, they can also be used for other purposes such as seat counting. While a lot of research has already been conducted on the topic of video analytics, the number of publications for sce- narios inside moving vehicles is quite limited. In [4], Milcent et al. present a system to detect baggage in transit vehicles. They preprocess the video stream to correct the lighting. A light location mask, indicating reflecting metallic posts inside the vehicle, is used to gather the different parts of one object. To increase the speed of the segmentation algorithm, it is only applied on a region indicated by a probability location mask. Several projects, such as PRISMATICA (Pro-active Integrated Systems for Security Management by Technological, Institu- tional and Communication Assistance, [5]) and BOSS (On- Board wireless Secured video Surveillance, [6]) mention the transmission of video feeds upon the triggering of an alarm, but do not describe how the alarm is exactly triggered. In [7], Vu et al. present an event recognition system based on face detection and tracking combined with audio analysis. Three dimensional (3-D) context such as zones of interest and static objects are recorded in a knowledge base and 3-D positions are calculated for mobile objects using calibration matrices. Strong changes in lighting conditions occasionally prevent the system to detect people correctly. Yayahiaoui et al. [8] and Liu et al. [9] report high accuracies in passenger counting using a dedicated setup. Since the cameras used for this setup can not be used for other purposes, this solution is too expensive to be used in some real life scenarios. Also, it is impossible to retrieve the location of the passengers. In a previous paper [10], we proposed a system to tackle the problem of seat counting. The main disadvantages are that manual labor is needed for each camera view and a training phase is necessary. In this paper, we propose two automatic wavelet-based available seat counting algorithms that extract and combine illumination invariant scene features efficiently into their composed background reference frame. The remainder of this paper is organized as follows. In Section II, we discuss our previous work of a non-automatic available seat counting algorithm. In Section III, we describe the two wavelet based available seat counting algorithms. An evaluation of the previously described non-automatic algo- rithm, the two algorithms described in this paper and a Simple Mixture of Gaussian Models (SMM) [11] based algorithm is given in Section IV. Finally, conclusions and future work are given in Section V. II. NON- AUTOMATIC AVAILABLE SEAT COUNTING In [10], we presented an approach to tackle the available seat counting problem. This approach consists of two stages: object detection and event detection. The object detection consists of three consecutive steps: first, Laplacian edge detection is applied to discover the