Object detectors have come a long way and are used for various applications. In pictures and videos, an object detector must deal with the background. In some settings, this background is indicative of the object; in others, it’s not and can even be disruptive. For models trained on data containing correlations between objects and backgrounds (background bias), it makes sense that changing the background can disrupt learned correlations. This paper is interested in how sensitive object detectors are to background changes, specifically when the training data does not contain correlations between objects and backgrounds. Models were trained on carefully controlled synthetic data, so only the backgrounds differed and correlations could be controlled. The results show that models perform better when tested with seen backgrounds than unseen backgrounds. This performance difference diminishes when the model is trained on more unique backgrounds.

Instance segmentation on data from Dynamic Vision Sensors (DVS) is an important computer vision task that needs to be tackled in order to push the research forward on these types of inputs. This paper aims to show that deep learning based techniques can be used to solve the task of instance segmentation on DVS data. A high performing model was used to solve this task, using event-based data that was transformed into RGB-D images. The chosen model for this work was Mask R-CNN, with an alteration for depth images, because of its high performance on frame based data. The N-MNIST dataset provides the event-based input, and the transformation of such an input is presented in this study. Furthermore, the masks are generated with the help of the MNIST dataset and heuristics are used for placing them at the correct positions. The results are promising and comparable to other results from literature on the task of semantic segmentation.

Event-based cameras represent a new alternative to traditional frame based sensors, with advantages in lower output bandwidth, lower latency and higher dynamic range, thanks to their independent, asynchronous pixels. These advantages prompted the development of computer vision methods on event data in the last decade, however event-based datasets are still in early stages in terms of size and complexity compared to normal datasets (e.g. ImageNet). This paper explores event data augmentation by superimposing two existing event datasets (N-MNIST and N-Caltech101) and by adding uniform noise. It shows that training an instance segmentation model on noisy datasets does not improve its performance, but the amount and type of noise added in the background decreases the performance of such model.

Event-based cameras do not capture frames like an RGB camera, only data from pixels that detect a change in light intensity, making it a better alternative for processing videos. The sparse data acquired from event-based video only captures movement in an asynchronous way. In this paper an evaluation is made on the efficiency and accuracy of object detection, specifically localization, between sparse and dense representations of data. Convolutional Neural Networks are used to train and test on images and event-based data. The results show a positive trade-off in terms of accuracy and efficiency for using sparse event-based data instead of dense data like images. These results provide a basis for an argument to use event-based cameras instead of RGB cameras when dealing with object detection.

The event-based camera represents a revolutionary concept, having an asynchronous output. The pixels of dynamic vision sensors react to the brightness change, resulting in streams of events at very small intervals of time. This paper provides a model to track objects in neuromorphic datasets, using clustering. In addition, a non-linear filter is applied to correct the estimation of the object position. Both single and multi-object tracking algorithms are provided and their performance is analyzed using different metrics, including the clustering evaluation scores and the tracking accuracy. The accuracy is over 0.6 for multi-target tracking and more than 0.7 for single object tracking. Besides the proposed model, a comparison between different possible approaches for event-based data tracking is provided.