  We present a novel adaptive host-chip modular architecture for video
acquisition to optimize an overall objective task constrained under a given bit
rate. The chip is a high resolution imaging sensor such as gigapixel focal
plane array (FPA) with low computational power deployed on the field remotely,
while the host is a server with high computational power. The communication
channel data bandwidth between the chip and host is constrained to accommodate
transfer of all captured data from the chip. The host performs objective task
specific computations and also intelligently guides the chip to optimize
(compress) the data sent to host. This proposed system is modular and highly
versatile in terms of flexibility in re-orienting the objective task. In this
work, object tracking is the objective task. While our architecture supports
any form of compression/distortion, in this paper we use quadtree
(QT)-segmented video frames. We use Viterbi (Dynamic Programming) algorithm to
minimize the area normalized weighted rate-distortion allocation of resources.
The host receives only these degraded frames for analysis. An object detector
is used to detect objects, and a Kalman Filter based tracker is used to track
those objects. Evaluation of system performance is done in terms of Multiple
Object Tracking Accuracy (MOTA) metric. In this proposed novel architecture,
performance gains in MOTA is obtained by twice training the object detector
with different system generated distortions as a novel 2-step process.
Additionally, object detector is assisted by tracker to upscore the region
proposals in the detector to further improve the performance.
