マーケティングにおいて、店内の顧客の姿勢推定は注目を集める分野の1つです。顧客の姿勢情報は顧客の商品への関心を推定に役立ちます。 例えば、顧客の体の向きが商品棚と向かい合っている時、顧客はその商品に対して興味があると考えられます。 反対に、体の向きが商品棚と平行な時は興味は小さいです． この考え方をもとに、監視カメラの画像から深層学習を用い顧客の姿勢を推定します． このシステムは、最初に畳み込みニューラルネットワーク(CNN)に関節ヒートマップを生成します． これに基づき、顧客の姿勢推定を行います．さらに、双方向リカレントニューラルネットワーク(BRNN)により前後の情報を入れることで精度向上を行います． その結果、時間情報を考慮した姿勢推定が行われます．最後に、システムの有効性を示すために店内以外のデータを用いた比較実験を行っています．

The analysis of customer pose draws more and more attention of retailers and researchers, because this information can reveal the customer habits and the customer interest level to the merchandise. In the retail store environment, customers’ poses are highly related to their body orientations. For example, when a customer is picking an item from merchandise shelf, he or she must face to the shelf. On the other hand, if the customer body orientation is parallel to the shelf, this customer is probably just walking through. Considering this fact, we propose a customer pose estimation system using orientational spatio-temporal deep neural network from surveillance camera. This system first generates the initial joint heatmaps using a fully convolutional network. Based on these heatmaps, we propose a set of novel orientational message-passing layers to fine-tune joint heatmaps by introducing the body orientation information into the conventional message-passing layers. In addition, we apply a bi-directional recurrent neural network on top of the system to improve the estimation accuracy by considering both forward and backward image sequences. Therefore, in this system, the global body orientation, local joint connections, and temporal pose continuity are integrally considered. At last, we conduct a series of comparison experiments to show the effectiveness of our system.