Can an outward facing camera on a bus, be used to recognize its location in GPS denied environment? Observit, provides cloud-based mobile surveillance services for bus operators using IP cameras with wireless connectivity. With the continuous gathering of video information, it opens up new possibilities for additional services. One service is to use the information with the technology, visual place recognition, to locate the vehicle, where the image was taken. The objective of this thesis has been to answer, how well can learnable visual place recognition methods localize a bus in a GPS denied environment and if a lightweight model can achieve the same accurate results as a heavyweight model. In order to achieve this, four model architecture has been implemented, trained and evaluate on a created dataset of interesting places. A visual place recognition application has been implemented as well, in order to test the models on bus video footage. The results show that the heavyweight model constructed of VGG16 with Patch-NetVLAD, performed best on the task with different recall@N values and got a recall@1 score of 92.31%. The lightweight model that used the backbone of MobileNetV2 with Patch-NetVLAD, scored similar recall@N results as the heavyweight model and got the same recall@1 score. The thesis shows that, with different localization methods, it is possible for a vehicle to identify its position in a GPS denied environment, with a model that could be deploy on a camera. This work, impacts companies that rely on cameras as their source of service.