In a context of European rail traffic growing rapidly, infrastructure managers are pushed to develop reliable solutions to improve safety and operational performance. This is a challenging task given 229853 km of network (28070 km in France only) [1]. For instance, in 2018, the total maintenance and renewable expenditure exceeds €20.6 billion in Europe (€5.4 billion for France) [2] and this is continuing to rise.In the present study we focus on the bogie component, a complex and important element in rail-road cars. Overheated rail-road car wheels and bearings known as hot boxes, are a major threat for any railway operation. Extensive research  have been done, where remote and contactless condition monitoring technologies have been developed [3]. Among them, a class of system are based on thermal sensors, such as hot box detector (HBD) currently installed in the European railway. Such systems involve a high installation and maintenance cost. Furthermore, they are dependent to other facilities, like triggers to activate and deactivate the system. So, cost-less, robust and easy to maintain critical systems monitoring solutions have to be investigated.With the advancements in both image sensor technology and processing capabilities, machine vision-based techniques may provide cost-effectiveness and easier solution for hot wheels and hot axle bearings detection.In this research work, automatic detection,tracking and counting of hot boxes is addressed through the implementation of way side infrared thermal cameras. First a discussion on thermal cameras required performances will be proposed and new uncooled fast pixel sensors will be introduced.  Implementation on a real railway will be presented and discussed. Then, some image processing methods ([4], [5]) developed and studied in this work will be presented and applied to infrared thermal images (IRTIs) taken by different wayside camera models. Finally, the advantages of remote way-side thermal cameras with deep learning techniques will be discussed and perspectives will be proposed.