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In this document, I first analyze some of the reasons why real-world environment perception is stillstrongly inferior to human perception in overall accuracy and reliability. In particular, I focus on thetask of object detection in traffic scenes and present an argument why this task is in fact a good modeltask for other, related perception problems (e.g. in robotics or surveillance). Enumerating the difficultiesencountered in this model task (and therefore, by inference, in many other detection tasks as well), I cometo the conclusion that problems in object detection can in fact be, to a significant extent, traced backto problems of the learning algorithms that are used in various forms when performing object detection.Namely, the lack of a probabilistic interpretation, the lack of incremental learning capacity, the lack oftraining samples and the inherent ambiguity of local pattern analysis are identified and used to justify aroad map for research efforts aimed at overcoming these problems. I present several of my works concerningreal-world applications of machine learning in perception, where the stated problems become very apparent.Subsequently, I describe in detail my recent research contributions and their significance in the context ofthe proposed road map: context-based object detection, generative and multi-modal learning as well as anoriginal method for incremental learning. The document is concluded by an outlook that addresses furtherwork to complete the road map, and the possibilities that are offered by such an endeavour in the field of machine perception.

Many state of the art object classification applications require many data samples, whose collection is usually a very costly process. Performing initial model training with synthetic samples (from virtual reality tools) has been proposed as a possible solution, although the resulting classification models need to be adapted (fine-tuned) to real-world data afterwards. For this bootstrapping process, we propose to use an incremental learning algorithm from the cognitive robotics domain which is particularly suited for perceptual problems. We apply it to a pedestrian detection problem where a synthetic dataset is used for initial training, and two different real-world datasets for fine-tuning and evaluation. The proposed scheme greatly reduces the number of real-world samples required while maintaining high classification accuracy. We additionally demonstrate several innovative incremental learning schemes for object detection, the basic idea being that usually only very few background samples are actually similar to pedestrian samples. By suitable arrangement of incremental learning steps, we can keep classification models simple by representing only such “hard” background samples.