  Event cameras are activity-driven bio-inspired vision sensors, thereby
resulting in advantages such as sparsity,high temporal resolution, low latency,
and power consumption. Given the different sensing modality of event camera and
high quality of conventional vision paradigm, event processing is predominantly
solved by transforming the sparse and asynchronous events into 2D grid and
subsequently applying standard vision pipelines. Despite the promising results
displayed by supervised learning approaches in 2D grid generation, these
approaches treat the task in supervised manner. Labeled task specific ground
truth event data is challenging to acquire. To overcome this limitation, we
propose Event-LSTM, an unsupervised Auto-Encoder architecture made up of LSTM
layers as a promising alternative to learn 2D grid representation from event
sequence. Compared to competing supervised approaches, ours is a task-agnostic
approach ideally suited for the event domain, where task specific labeled data
is scarce. We also tailor the proposed solution to exploit asynchronous nature
of event stream, which gives it desirable charateristics such as speed
invariant and energy-efficient 2D grid generation. Besides, we also push
state-of-the-art event de-noising forward by introducing memory into the
de-noising process. Evaluations on activity recognition and gesture recognition
demonstrate that our approach yields improvement over state-of-the-art
approaches, while providing the flexibilty to learn from unlabelled data.
