Behavior recognition is already starting to move towards some scenarios, although it needs to be more sophisticated technically.

       According to the "2019-2024 Research Report on China's Machine Vision Industry Prospects and Investment Opportunities" released by the China Business Industry Research Institute, the scale of China's machine vision market exceeded 10 billion yuan for the first time in 2018; , the machine vision market will further expand,It is estimated that the machine vision market will reach nearly 12.5 billion yuan in 2019.

      It is true that the market size of the CV (machine vision) industry is not small and profitable, but when technology products mature and start to be applied, how to eat this cake has become the biggest problem faced by many CV startups. . At the same time, continuous losses and profit pressure are also urging every CV company to “run”.

       趨視科技并不屬于CV領域最知名的行業，然而它們卻在落地應用和盈利上先人一步，其公司創始人徐飆表示："If 90% of companies in the industry are losing money, we belong to the other 10%."

        how do they do it

Figure | Xu Biao, founder of Truthvision Technology

CV not only has face recognition, but also behavior analysis

      When it comes to CV, attention and topics are often concentrated in the field of face recognition. SenseTime, Megvii, etc. are the focus of attention both inside and outside the industry, but CV is not equivalent to face recognition, it also includes behavior recognition. Xu Biao introduced that since its establishment, Truthvision Technology has always aimed at behavior recognition.

       “行為識別就是識別人類或者車的行為，Such as people's fighting behavior, car running red light behavior and so on. Although both belong to machine vision, face recognition and behavior recognition are two technologies and different fields. "

At the technical level, face recognition can be completed through a photo, while behavior recognition needs to be judged by combining continuous data, because behavior itself is a continuous and dynamic process.in short,Face recognition solves the problem of who the target is, and behavior solves what kind of thing.At present, behavior recognition is often used in judicial management, smart stores,intelligent

       Xu Biao told us: "There are many fields where behavior recognition is applicable, but because the technology is not mature enough, it is difficult for behavior recognition technology to play a very good role in the face of too complex and non-standard scenes. Therefore, this technology can only It is first applied in some vertical scenarios, and gradually accumulated and improved in the process of application, so as to expand to more scenarios, and finally meet the requirements of human behavior cognition in a large range."

        So what are the technical difficulties of behavior recognition?

        Since behavior is diverse, it includes individual behavior and group behavior, and each behavior is expressed in different ways. For example, fighting and stealing, fighting between individuals and fighting between groups are completely different.Therefore, behavior recognition faces great difficulties at the data collection level, which mainly involve problems such as occlusion and dislocation.

        At the same time, the angle of human viewing the world is three-dimensional, and the picture captured by the camera is two-dimensional, so there will be a person in the video showing an arm, but because the distance parameter cannot be collected in the video,所以遮擋、錯位的現象會讓AI算法難以判斷。

       其次學習數據欠缺。眾所周知，許多AI技術依靠深度學習算法模型去訓練，這導致要讓AI實現行為識別，就必須先給行為下定義，讓AI知道行為是什么。然而前面已經提到行為非常復雜，甚至很多時候AI需要學習判斷的是負面行為，因此企業很難獲取到大量的學習數據。而算法模型沒有經過大量數據去訓練，也就很難“聰明”起來，從而在識別的效果和精度上難以達到用戶需求。

        不過盡管在技術上需要更加精進，但行為識別已經開始走向一些場景。

CV企業破冰關鍵：規模化

       徐飆介紹：“公司一開始關注的就是行業落地而非通用場景，且瞄準的第一個領域就是司法領域行業的管理，比如監獄管理，是否有犯人打斗、翻墻、攀爬等。這對于司法領域的管理而言是一個剛需，能夠降低人力管理成本，提升管理質量。”

       而行業落地和通用場景落地兩條路徑的最大區別，在徐飆看來，前者能夠助力企業快速實現規模化落地，而這至關重要。

       他談到：“所有CV廠商在近年來特別強調落地，本質上就是規模化落地，即企業在一個項目試點實現技術落地后能夠快速復制到下一個同類型的場景中，而不是做完一個試點，下一個場景再重新做一遍，這無疑增加了許多成本。”

       對于企業而言，要實現規模化落地首先在最初尋找落地行業時，就要找到能夠實現規模化、可復制性強的場景。其中的關鍵在于，企業對于用戶核心訴求的把握是否精確。徐飆認為，CV企業要實現規模化必須了解用戶的需求，所謂需求指的不僅是用戶對于功能的需求，還包括用戶對性能當中準確度的要求。

       “這需要碰撞。有些時候沒有人會告訴你他的需求和對準確度的要求是什么，企業往往需要通過試點、交流、反饋、修正......逐步形成一個行業共識，而并非單個客戶的需求。”

       但即便把握了用戶需求和性能指標并不足夠，企業還要評估自身的技術體系、優勢能否滿足用戶的需求和指標。最后企業還要考慮實現規模化之后，是否會被競品取代，這要求其必須在技術落地應用過程中打造自身的技術門檻，如此廠商們才能率先占領市場，并在后續的競爭中獲勝。

       回到趨視科技自身，徐飆談道：“公司明年的短期計劃，一方面是確保在司法行業實現規模化，創造更多的收益；同時也會將技術落地到智慧門店場景。小規模化帶給我們盈利，也驗證了技術已經達到可復制狀態，所以我們將會向更大的市場進行布局。”

【鎂客·請講】欄目   策劃&撰寫：溫暖