# Place Recognition: An Overview of Vision Perspective
## 4 Discussion and Future Work  
Place recognition has made great advances in the last few decades, e.g., on the principles of how animals recognize and remember places, and the relationship between places from a neuroscience perspective [41]; a new way of describing places using convolutional neural networks, and a number of datasets specifically for places were put forward. However, we are still a long way from a robust long-term visual place-recognition system that can be applied well to a variety of scenarios of real-world places. Hence, we highlight several promising avenues of ongoing and future research that are leading us closer to this outcome. Place recognition is becoming a hot research topic and it is benefiting from related ongoing works in other fields, especially the enormous successes achieved in computer vision through deep-learning techniques, e.g., image classification, object detection, and scene recognition. While features extracted from pretrained CNNs on other vision tasks are shown to be transferable and versatile, they have, so far, yielded unsatisfactory performance on place-recognition tasks. There is a high possibility that we are still not fully exploiting the potential of CNNs. We can improve their performance in two aspects. First, by gathering a sufficient amount of place-centric data covering various environments, including illumination, weather, structure, seasons, and viewpoint changes. An alternative and reliable source is Google Street View Time Machine. If you train a CNN on a small-size dataset, the model usually works awfully on other datasets, since place recognition is dataset-dependent. One also needs to retrain it when a new dataset is fed into it. Since one of the advantages of CNNs is to extract representative features through Big Data, their state-of-the-art performance can be improved. Second, designing optimized CNN architecture for place-recognition tasks. Real-world images from cameras are usually high-resolution, whereas in many cases one needs to downscale the original images. For example, the input size of VGG-16 is 224 \(\times\) 224. Moreover, an architecture that is well-suited for object detection may not fit well into place-recognition tasks since their visual cues are different. Designing a good loss function is also essential to their features. Developments focused on the above two problems would further improve the robustness and performance of existing deep-learning-based place-recognition techniques. Place-recognition systems can also benefit from ongoing research on object detection, scene classification [42], and scene recognition [16]. Semantic context from a scene, interpreted as the \"gist\" of the scene, can help to partition the search space when comparing similarities between image representations, which ensures scalability and real-time processing towards real-world application. Note that different places may share a common semantic concept that needs a further and more precise feature-mapping procedure. Objects such as pedestrians and trees should be avoided, while objects like buildings and landmarks are important for long-term place recognition. Automatically determining and suppressing features that lead to confusion in visual place-recognition systems would also improve place-recognition performance. Conceptualization, Z.Z. and J.Z. ; methodology, X.W., C.Z., and J.Z. ; formal analysis, X.W. and Y.C. ; investigation, X.W. and C.Z. ; resources, Z.Z. and J.Z. ; writing--original draft preparation, C.Z. and J.Z. ; writing--review and editing, Y.C. ; supervision, Z.Z. ; project administration, Z.Z. ; funding acquisition, Z.Z. and J.Z. This research was funded by the National Natural Science Foundation of China (grant no. 61871464); the National Natural Science Foundation of Fujian Province, China (grant no. 2016J01324); and the Zhejiang Provincial Natural Science Foundation of China (grant number LY17F020009).