  Retailers have long been searching for ways to effectively understand their
customers' behaviour in order to provide a smooth and pleasant shopping
experience that attracts more customers everyday and maximises their revenue,
consequently. Humans can flawlessly understand others' behaviour by combining
different visual cues from activity to gestures and facial expressions.
Empowering the computer vision systems to do so, however, is still an open
problem due to its intrinsic challenges as well as extrinsic enforced
difficulties like lack of publicly available data and unique environment
conditions (wild). In this work, We emphasise on detecting the first and by far
the most crucial cue in behaviour analysis; that is human activity detection in
computer vision. To do so, we introduce a framework for integrating human pose
and object motion to both temporally detect and classify the activities in a
fine-grained manner (very short and similar activities). We incorporate partial
human pose and interaction with the objects in a multi-stream neural network
architecture to guide the spatiotemporal attention mechanism for more efficient
activity recognition. To this end, in the absence of pose supervision, we
propose to use the Generative Adversarial Network (GAN) to generate exact joint
locations from noisy probability heat maps. Additionally, based on the
intuition that complex actions demand more than one source of information to be
identified even by humans, we integrate the second stream of object motion to
our network as a prior knowledge that we quantitatively show improves the
recognition results. We empirically show the capability of our approach by
achieving state-of-the-art results on MERL shopping dataset. We further
investigate the effectiveness of this approach on a new shopping dataset that
we have collected to address existing shortcomings.
